Search results for """ "Guesstimation""

Book SynopsisTHINKING CAPS ON: Jupiter has more moons than any other planet in our solar system, how many moons does it have? All answers in the game are numeric, so it's up to you to get your guesstimate as close to the answer as possible, so you don't have to know the answer you just have to be closer than anyone else. Don't know the answer? It doesn't matter, because you never know, your guess could win the card. Everyone writes down their answer without the other players seeing the guess... the player with the closest answer wins the round and keeps the card... the first player to collect 12 cards wins the game.

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Book SynopsisEnables anyone with basic math and science skills to estimate virtually anything - quickly - using plausible assumptions and elementary arithmetic. This book presents an array of estimation problems that range from devilishly simple to quite sophisticated and from real-world concerns to silly ones.Trade Review"Dr. Adam and his colleague Lawrence Weinstein, a professor of physics, offer a wide and often amusing assortment of Fermi flexes in a book that just caught my eye, Guesstimation: Solving the World's Problems on the Back of a Cocktail Napkin."--Natalie Angier, New York Times "An important skill of great use ... is the ability to derive an approximate result from insufficient data. Guesstimation is a collection of [problems] gathered from everyday life and various fields. Working out questions ... is both entertaining and enlightening. It may also help foster your career ... because making correct guesses quickly establishes your reputation as an expert."--Stephan Mertens, Science "This book is a stimulating collection that will help the reader to reach informed judgments and will be a useful source of inspiration for mathematics and physics teachers: my only concern is that if my students have read it before they arrive at university, I may have to find a new approach to my first day's teaching."--Tony Mann, Times Higher Education "While few can hope to emulate the brilliance of a Nobel Prize winner like [Enrico] Fermi, coming up with pretty good guesstimates is a skill that can be taught. And that's the aim of Guesstimation. After a quick tutorial, the authors get down to business with a host of wide-ranging worked examples, from estimating the numbers of piano tuners in Los Angeles to figuring out the impact of deforestation on greenhouse gas levels. The results are sometimes surprising."--Robert Matthews, BBC Focus Magazine "[Guesstimation is] a left-brain book that helps you approximate answers to the types of questions actually asked in some job interviews today."--Peter Coy, BusinessWeek "[A] delightful account of mathematical approximation, which instills the beauty and power of the back-of-the-envelope calculation. The puzzles make addictive confidence builders by breaking down tricky questions into manageable parts. Never again will you take a newspaper figure at face value without feeling the need, and confidence, to guesstimate your own figure."--Matthew Killeya, New Scientist "Guesstimation is both enlightening and entertaining. I recommend it to my fellow journalists both as a tool of our trade and as a mind stretcher."--Rony V. Diaz, Manila Times "Any idea what fraction of land in the US is covered by either a roof or pavement? Known as a Fermi problem, this type of question requires the use of reasonable estimation, which is the focus of the book at hand. In the initial chapters, Weinstein and Adam briefly review good 'guesstimation' techniques involving numbers and explain why the use of the geometric mean is preferred over the arithmetic mean."--J. Johnson, Choice "How many people in the world are picking their nose right now? Weinstein and Adam 'guesstimate' the answer to this problem and 79 others, covering chemistry, physics, biology and history. The book is a step-by-step guide to problem-solving using rough-and-ready maths, the kind done on the back of a cocktail napkin. And the authors have kindly left additional questions at the end to get readers started on their own problem-solving expedition."--Cosmos "Physics educators can use this book as a guide to including the important skill of estimation in their courses. Students may find the power of estimation to be a valuable skill and will want to work their way through this book."--Arthur Eisenkrafr, American Journal of Physics "A source of imaginative problems, this book would make a nice addition to a mathematics department library."--Diane Resek, Mathematics Teacher "[I]t's quite obvious that the authors intend their book to be fun, nonthreatening, and user-friendly. There's very little not to like... [T]he book can be for everybody, 'higher-up professionals' who might know math but not physics, as well as students wrestling with 'word problems.' Teachers could very well recommend it to math majors and nonmajors alike, or even use it in the classroom, in some cases as supplementary reading for the course."--Marion Deutsche Cohen, Mathematical Intelligencer "The cumulative effect of fairly simple paths to estimating solutions to a dizzying array of difficult problems is fascinating."--Ray Bert, Civil Engineering "This book will be enjoyed by anyone with an interest in estimation, but is also targeted at those applying for jobs at companies like Google, where the kind of questions considered in the book are often used in the interview process."--Paul Taylor, Mathematics TodayTable of ContentsAcknowledgments xi Preface xiii Chapter 1: How to Solve Problems 1 Chapter 2: Dealing with Large Numbers 11 2.1 Scientific Notation 11 2.2 Accuracy 14 2.3 A Note on Units 16 2.4 Unit Conversion 17 Chapter 3: General Questions 19 3.1 One big family 21 3.2 Fore! 25 3.3 This is a fine pickle you've got us into, Patty 29 3.4 Throwing in the towel 31 3.5 Hey buddy, can you fill a dome? 35 3.6 A mole of cats 39 3.7 Massive MongaMillions 41 3.8 Tons of trash 43 3.9 Mt. Trashmore 47 3.10 Juggling people 51 3.11 Shelving the problem 53 Chapter 4: Animals and People 55 4.1 More numerous than the stars in the sky 57 4.2 Laboring in vein 61 4.3 Unzipping your skin 65 4.4 Hair today, gone tomorrow 69 4.5 Hot dawg! 73 4.6 Playing the field 75 4.7 Ewww... gross! 77 4.8 Going potty 79 4.9 Let's get one thing straight! 83 Chapter 5: Transportation 87 5.1 Driving past Saturn 89 5.2 Drowning in gasoline 91 5.3 Slowly on the highway 95 5.4 Rickshaws and automobiles 99 5.5 Horse exhaust 103 5.6 Tire tracks 107 5.7 Working for the car 109 Chapter 6: Energy and Work 113 6.1 Energy of height 114 6.1.1 Mountain climbing 115 6.1.2 Flattening the Alps 119 6.1.3 Raising a building 123 6.2 Energy of motion 126 6.2.1 At your service 127 6.2.2 Kinetic trucking 129 6.2.3 Racing continents 131 6.2.4 "To boldly go... " 135 6.3 Work 138 6.3.1 Crash! 139 6.3.2 Spider-Man and the subway car 143 Chapter 7: Hydrocarbons and Carbohydrates 145 7.1 Chemical energy 145 7.1.1 Energy in gasoline 147 7.1.2 Battery energy 151 7.1.3 Battery energy density 155 7.1.4 Batteries vs. gas tanks 159 7.2 Food is energy 162 7.2.1 Eat here, get gas 163 7.2.2 Farmland for ethanol 167 7.3 Power! 170 7.3.1 Hot humans 171 7.3.2 Fill 'er up with gasoline 173 7.3.3 Fill 'er up with electricity 175 Chapter 8: The Earth, the Moon, and Lots of Gerbils 179 8.1 "And yet it moves" (e pur si muove) 181 8.2 Duck! 185 8.3 Super-sized Sun 189 8.4 Sun power 193 8.5 Gerbils 1, Sun 0 197 8.6 Chemical Sun 201 8.7 Nearby supernova 205 8.8 Melting ice caps 209 Chapter 9: Energy and the Environment 213 9.1 Power to the people 215 9.2 Continental power 219 9.3 Solar energy 223 9.4 Land for solar energy 225 9.5 Tilting at windmills 229 9.6 The power of coal 233 9.7 The power of nuclei 237 9.8 Hard surfaces 239 Chapter 10: The Atmosphere 243 10.1 Into thin air 245 10.2 Ancient air 247 10.3 Suck it up 251 10.4 CO2 from coal 255 10.5 A healthy glow 259 10.6 CO2 from cars 261 10.7 Turning gas into trees 265 10.8 Turning trees into gas 269 Chapter 11: Risk 273 11.1 Gambling on the road 275 11.2 The plane truth 277 11.3 Life's a beach 279 11.4 Up in smoke 281 Chapter 12: Unanswered Questions 285 Appendix: Needed Numbers and Formulas 289 A.1 Useful Numbers 289 A.2 Handy Formulas 289 A.3 Metric Prefixes 290 B Pegs to Hang Things On 291 Bibliography 295 Index 299

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Book SynopsisReveals the simple techniques needed to estimate virtually anything and illustrates them using an eclectic array of problems. This title shows how to estimate everything from how closely you can orbit a neutron star without being pulled apart by gravity, to the fuel used to transport your food from the farm to the store.Trade Review"This follow-up to the popular Guesstimation offers more on the joy of mathematical estimation, and inspiration for the budding analyst."--Nature "The books do a wonderful job at helping the reader to master the craft."--Cut the Knot Insights "A delightful volume... I hope to be able to use many of the tricks I learned in the future. I also hope to teach some of them to students. This would make a great secondary textbook in many classes, ranging from quantitative literacy to a science methods class for future educators. A careful study of this book would certainly improve a student's ability to take a complicated question, break it down into solvable parts, and assemble the parts to find an answer. Because this is quite close to what I want my students to do when faced with a difficult problem in pure mathematics as well, I consider this to be a very valuable book indeed."--Dominic Klyve, MAA Reviews "Guesstimation 2.0: Solving Today's Problems on the Back of a Napkin succeeds where most popular science literature so often fails. This is because it provides its readers with a scientific tool they can use immediately in their everyday lives... [Makes] an excellent addition for the casual scientist, job interviewee, or anyone hoping to impress their friends at a party."--Gabriel Thoumi, Mongabay.com "Readers who enjoyed Weinstein's first volume will be pleased with this instalment."--Choice "Guesstimation 2.0 is a book that was made to mediate between fun and useful... Whether or not a fan of numbers, it's always cool to appear smart, therefore Guesstimation 2.0 is an excellent element to add to one's arsenal."--Sarthak Shankar, Organiser "Certainly a good read for any teacher who enjoys numbers and the world around us."--Mark Hughes, Mathematics Teaching in the Middle School "Guesstimation's problems are fun and engaging in character, and the solutions are intuitive and well explained. Each problem and solution stands independently, and is about four pages long, making the book ideal for passing a quick ten minutes, and easy to pick up and put down. If, like me, you like ill-posed questions to have concrete answers then Guesstimation is definitely a good place to hone your estimation skills!"--Fionntan Roukema, Mathematical SpectrumTable of ContentsAcknowledgments xi Preface xiii 1 How to Solve Problems 1 2 General Questions 11 *2.1 Who unrolled the toilet paper? 13 *2.2 Money height 17 *2.3 Blotting out the Sun 19 *2.4 Really extra-large popcorn 21 *2.5 Building volume 25 *2.6 Mass of money 29 *2.7 A baseball in a glass of beer 33 *2.8 Life on the phone 37 *2.9 Money under the bridge 41 *2.10 Monkeys and Shakespeare 45 *2.11 The titans of siren 49 *2.12 Airheads at the movies 53 *2.13 Heavy cars and heavier people 55 *2.14 Peeing in the pool 59 3 Recycling: What Really Matters? 63 *3.1 Water bottles 67 *3.2 99 bottles of beer on the wall ... 71 *3.3 Can the aluminum 75 *3.4 Paper or plastic? 79 *3.5 Paper doesn't grow on trees! 83 *3.6 The rain in Spain ... 87 *3.7 Bottom feeders 91 *3.8 You light up my life! 95 4 The Five Senses 101 *4.1 Don't stare at the Sun 103 *4.2 Men of vision 105 *4.3 Light a single candle 109 *4.4 Oh say can you see? 113 *4.5 Bigger eyes 117 *4.6 They're watching us! 121 *4.7 Beam the energy down, Scotty! 125 *4.8 Oh say can you hear? 131 *4.9 Heavy loads 135 5 Energy and Work 139 *5.1 Power up the stairs 143 *5.2 Power workout 145 *5.3 Water over the dam 149 *5.4 A hard nut to crack 153 *5.5 Mousetrap cars 155 *5.6 Push hard 159 *5.7 Pumping car tires 161 *5.8 Pumping bike tires 165 *5.9 Atomic bombs and confetti 169 6 Energy and Transportation 173 *6.1 Gas-powered humans 177 *6.2 Driving across country 181 *6.3 Keep on trucking 185 *6.4 Keep on biking 189 *6.5 Keep on training 193 *6.6 Keep on flying 197 *6.7 To pee or not to pee 201 *6.8 Solar-powered cars 205 *6.9 Put a doughnut in your tank 209 *6.10 Perk up your car 213 *6.11 Don't slow down 217 *6.12 Throwing tomatoes 219 7 Heavenly Bodies 223 *7.1 Orbiting the Sun 227 *7.2 Flying off the Earth 229 *7.3 The rings of Earth 233 *7.4 It is not in the stars to hold our destiny 237 *7.5 Orbiting a neutron star 241 *7.6 How high can we jump? 245 *7.7 Collapsing Sun 249 *7.8 Splitting the Moon 253 *7.9 Splitting a smaller moon 257 *7.10 Spinning faster and slower 263 *7.11 Shrinking Sun 267 *7.12 Spinning Earth 271 *7.13 The dinosaur killer and the day 273 *7.14 The Yellowstone volcano and the day 277 *7.15 The orbiting Moon 281 *7.16 The shortest day 283 8 Materials 289 *8.1 Stronger than spider silk 291 *8.2 Beanstalk to orbit 295 *8.3 Bolt failure 299 *8.4 Making mountains out of molecules 303 *8.5 Chopping down a tree 307 9 Radiation 311 *9.1 Nuclear neutrinos 315 *9.2 Neutrinos and you 319 *9.3 Solar neutrinos 323 *9.4 Supernovas can be dangerous 327 *9.5 Reviving ancient bacteria 331 *9.6 Decaying protons 335 *9.7 Journey to the center of the galaxy 337 Appendix A * Dealing with Large Numbers 341 * A.1 Large Numbers 341 * A.2 Precision, Lots of Digits, and Lying 343 * A.3 Numbers and Units 345 Appendix B * Pegs to Hang Things On 347 Bibliography 351 Index 355

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Book SynopsisEstimating software development often produces more angst than value, but it doesn't have to. Identify the needs behind estimate requests and determine how to meet those needs simply and easily. Choose estimation techniques based on current needs and available information, gaining benefit while reducing cost and effort. Detect bad assumptions that might sink your project if you don't adjust your plans. Discover what to do when an estimate is wrong, how to recover, and how to use that knowledge for future planning. Learn to communicate about estimates in a healthy and productive way, maximizing advantage to the organization and minimizing damage to the people. In a world where most developers hate estimation and most managers fear disappointment with the results, there is hope for both. It requires giving up some widely held misconceptions. Let go of the notion that "an estimate is an estimate" and estimate for the particular need you, and your organization, have. Realize that estimates have a limited shelf-life, and reestimate frequently if it's important. When reality differs from your estimate, don't lament; mine that disappointment for the gold that can be the longer-term jackpot. Estimate in comparison to past experience, by modeling the work mathematically, or a hybrid of both. Learn strategies for effective decomposition of work and aspects of the work that likely affect your estimates. Hedge your bets by comparing the results of different approaches. Find out what to do when an estimate proves wrong. And they will. They're estimates, after all. You'll discover that you can use estimates to warn you of danger so you can take appropriate action in time. Learn some crucial techniques to understand and communicate with those who need to understand. Address both the technical and sociological aspects of estimation, and you'll help your organization achieve its desired goals with less drama and more benefit. What You Need: No software needed, just your past experience and concern for the outcomes.

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Book SynopsisOn the 10th anniversary of a teenage girl?s disappearance, her cold case breaks open in dangerous ways…and threatens to tear apart her small Wisconsin town all over again in the masterfully twisty new psychological suspense novel from the internationally bestselling author of Twenty Years Later.For fans of Riley Sager, Anna Downes, Alex Finlay, Stacy Willingham, and Karin Slaughter.Ten years ago, 17-year-old high school volleyball star Callie Jones vanished from her quiet Wisconsin lake community. A highly publicized search followed but her body was never found. The case went cold, but the echoes still linger.Ethan Hall, a former renegade detective turned ER doctor, left law enforcement to escape the horrors of the kid crime division. But on the tenth anniversary of Callie?s disappearance, his former partner, Pete Kramer, makes a desperate request. Pete is the veteran detective who originally investigated the case. Now he?s dying, and to ease his conscience and get closure for the Jones family, he needs Ethan to return to the haunting work he left behind?and solve what happened to Callie, once and for all.Word soon spreads and everyone in the small town of Cherryview feels a rush of hope that answers will finally be found. Amid a sweltering heatwave, Ethan?s investigation gains momentum, but reexamining old evidence won?t be enough. He needs a new way into the case, no matter how dangerous or unconventional.Soon Ethan?s methods draw him deeper into a twisted psychological game. Because there is much more to the nightmare of Callie?s disappearance than he imagined, including a connection with his own dark past . . . and secrets that are still worth killing for.

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Book SynopsisAsk the right questions and guess what’s on your head when you play Guessing Things from Petit Collage!Created in collaboration with the team behind the New York Times bestselling book Invisible Things, this easy-to-learn guessing game is fun for the whole family. This playful twist on the beloved game of What Am I? is also a powerful yet playful tool for kids to name and engage with their emotions as well as think about all the things they can’t see. Simply pop a card into another player’s glasses and get to guessing! Half the deck will help children understand and talk about Invisible Things like emotions, sounds, and dreams as they ask thoughtful questions: “Am I a feeling?” “Do I feel this a lot?” “Does this make me happy?” “Am I love?” “YES!” The other half of the deck encourages awareness of what’s in the world around them as they ask silly questions: “Am I food?” “Am I cheesy?” “Do I like pineapple on this?” “Am I pizza?” “YES!” Made for 2–4 players, ages 6+, you choose whether to play against the clock and keep things moving fast or pick a slower pace to think and talk about what's on your cards. Guessing Things improves patience and focus, encourages emotional development, and helps children understand turn-taking and winning and losing with grace. Inside the box, you’ll find 30 Invisible Things cards, 20 Things cards, 4 pairs of card glasses (2 for kids, 2 for grown-ups), a guessing guide sheet, and English instructions. The game is made using FSC paper, and the packaging is printed using 75% recycled materials and vegetable inks.AIM OF THE GAME: Be the first player to guess what’s on your head!HOW TO PLAY: First, everyone pops on a pair of glasses. Second, shuffle the deck and then deal one card to each player to slot into the glasses of the person to their left. Make sure no one sees what’s on their own head! Decide who will go first and whether you want to play against the clock or not. If you do, then grab a timer, set it for two minutes, and press start … NOW! Ask yes or no questions to guess what's in your glasses. Look at our sample questions for help if you need it. Anyone can answer your question, but they can only answer “Yes” or “No.” If you’re using a timer and correctly guess what’s on your head before the time runs out, congratulations—you’ve won your round and get to keep the card! If you don’t guess it within the time, then keep the card in your glasses until it’s your turn again. Play then moves clockwise for the next player to ask their questions. If you’re not using a timer, then players take turns asking questions. For every yes answer, you can ask another question. But if the answer is no, your turn is over and play moves clockwise for the next person to ask their questions. The first player to correctly guess what's in their glasses wins the game!  WHO IT'S FOR: Perfect for 2–4 players, ages 6 and up.WHAT'S INSIDE: 30 Invisible Things cards, 20 Things cards, 4 pairs of card glasses (2 for kids, 2 for grown-ups), a guessing guide sheet, and English instructions.

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Book SynopsisDoes your cat have whiskers? Is your cat cuddly? Do you think your cat looks evil? Is your cat Special Agent Mittens?This is a fun and feline twist to the classic tabletop guessing game. The rules are simple, but the process of elimination is purrfectly tricky!Each player gets a tray and a pack of beautifully illustrated cat cards. They display their cards in the slots on the tray and the players take turns to ask yes or no questions to deduce their opponent’s cat. The more interesting he questions the more fun you’ll have with the wonderfully inventive and eccentric range of feline characters found here - from Cindy Clawford to Picatso, Jennifur to Colonel Snazzypants. These cats will test your skills - and how well you know your opponent! - as you try to win the game and guess that kitty!

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Book Synopsis24 of your favourite Mario characters feature in this new edition of Hasbro's classic face guessing game.

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Book SynopsisFun lift-the-flaps foster the imagination and reveal colorful new images Images of different fruits transform into animals with lift-the-flaps in this interactive book. Readers of all ages will delight in trying to guess the next surprise. A pineapple becomes a yellow armadillo; a red apple turns into the face of a monkey. What will become of an orange, a banana, grapes, and more? The irresistible guessing game encourages children to imagine the possibilities.Trade Review"By lifting flaps, youngsters can transform fruit into animals. Yonezu's style, utilizing a thick, clumpy black line and bold, flat colors, is eye-catchingly simple without ever becoming boring. Surprising and engaging." -- Kirkus Reviews

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Book SynopsisHarry Van Trees s Detection, Estimation, and Modulation Theory, Part I is one of the great time-tested classics in the field of signal processing. This new edition has been thoroughly revised and expanded, making it again the most comprehensive and up-to-date treatment of the subject.Table of ContentsPreface xv Preface to the First Edition xix 1 Introduction 1 1.1 Introduction 1 1.2 Topical Outline 1 1.3 Possible Approaches 11 1.4 Organization 14 2 Classical Detection Theory 17 2.1 Introduction 17 2.2 Simple Binary Hypothesis Tests 20 2.3 m Hypotheses 51 2.4 Performance Bounds and Approximations 63 2.5 Monte Carlo Simulation 80 2.6 Summary 109 2.7 Problems 110 3 General Gaussian Detection 125 3.1 Detection of Gaussian Random Vectors 126 3.2 Equal Covariance Matrices 138 3.3 Equal Mean Vectors 174 3.4 General Gaussian 197 3.5 m Hypotheses 209 3.6 Summary 213 3.7 Problems 215 4 Classical Parameter Estimation 230 4.1 Introduction 230 4.2 Scalar Parameter Estimation 232 4.3 Multiple Parameter Estimation 293 4.4 Global Bayesian Bounds 332 4.5 Composite Hypotheses 348 4.6 Summary 375 4.7 Problems 377 5 General Gaussian Estimation 400 5.1 Introduction 400 5.2 Nonrandom Parameters 401 5.3 Random Parameters 483 5.4 Sequential Estimation 495 5.5 Summary 507 5.6 Problems 510 6 Representation of Random Processes 519 6.1 Introduction 519 6.2 Orthonormal Expansions: Deterministic Signals 520 6.3 Random Process Characterization 528 6.4 Homogeous Integral Equations and Eigenfunctions 540 6.5 Vector Random Processes 564 6.6 Summary 568 6.7 Problems 569 7 Detection of Signals–Estimation of Signal Parameters 584 7.1 Introduction 584 7.2 Detection and Estimation in White Gaussian Noise 591 7.3 Detection and Estimation in Nonwhite Gaussian Noise 629 7.4 Signals with Unwanted Parameters: The Composite Hypothesis Problem 675 7.5 Multiple Channels 712 7.6 Multiple Parameter Estimation 716 7.7 Summary 721 7.8 Problems 722 8 Estimation of Continuous-Time Random Processes 771 8.1 Optimum Linear Processors 771 8.2 Realizable Linear Filters: Stationary Processes, Infinite Past: Wiener Filters 787 8.3 Gaussian–Markov Processes: Kalman Filter 807 8.4 Bayesian Estimation of Non-Gaussian Models 842 8.5 Summary 852 8.6 Problems 855 9 Estimation of Discrete–Time Random Processes 880 9.1 Introduction 880 9.2 Discrete-Time Wiener Filtering 882 9.3 Discrete-Time Kalman Filter 919 9.4 Summary 1016 9.5 Problems 1016 10 Detection of Gaussian Signals 1030 10.1 Introduction 1030 10.2 Detection of Continuous-Time Gaussian Processes 1030 10.3 Detection of Discrete-Time Gaussian Processes 1067 10.4 Summary 1076 10.5 Problems 1077 11 Epilogue 1084 11.1 Classical Detection and Estimation Theory 1084 11.2 Representation of Random Processes 1093 11.3 Detection of Signals and Estimation of Signal Parameters 1095 11.4 Linear Estimation of Random Processes 1098 11.5 Observations 1105 11.6 Conclusion 1106 Appendix A: Probability Distributions and Mathematical Functions 1107 Appendix B: Example Index 1119 References 1125 Index 1145

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Book SynopsisParameter Estimation for Scientists and Engineers discusses estimating parameters of expectation models of statistical observations. It aims to show scientists and engineers, who often are not aware of estimators other than least squares, that statistical parameter estimation has much more to offer than least squares estimation alone.Trade Review“An indispensable tool for scholars and research workers in mathematics and the mathematical sciences.” (Mathematical Reviews, 2009) "Despite its lean size, the book is able to cover many of the techniques and theories in parameter estimation that are core to applied sciences, and so this is certainly a valuable reference for researchers and graduate students alike. The book's exposition is lucid, making it an accessible reading for someone with a reasonable background in elementary statistics. Thus I think anyone in applied sciences and engineering dealing with the implementation of expectation models and aiming to estimate model parameters will find this book helpful. This is a great addition to resources in learning or reviewing statistical tools that emphasize taking advantage of valuable information from data and improving the precision of estimation." (Technometrics, November 2008) "I highly recommend this book to practitioners who want to systematically learn and use, new, better techniques for parameter estimation." (Computing Reviews, September 10, 2008) "…appropriate for students in advanced applied statistics courses…even more useful as a supplemental resource…" (CHOICE, January 2008)Table of ContentsPreface. 1 Introduction. 2 Parametric Models of Observations. 3 Distributions of Observations. 4 Precision and Accuracy. 5 Precise and Accurate Estimation. 6 Numerical Methods for Parameter Estimation. 7 Solutions or Partial Solutions to Problems. Appendix A: Statistical Results. Appendix B: Vectors and Matrices. Appendix C: Positive Semidefinite and Positive Definite Matrices. Appendix D: Vector and Matrix Differentiation. References. Topic Index.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisGuess who''s getting dressed ... is it you?Who''s put on some stripy socks? Is it reindeer? No, it''s Fox!Who''s put on this spotty coat? Is it Cat? No, it''s Goat! A brilliantly interactive, stylish, young and fun Guess Who board book from beloved storyteller Smriti Halls and with deliciously vibrant artwork from internationally-renowned artist Marta Altés. Together they bring wit and joyful warmth to the daily routine of getting dressed. With funny animals cleverly disguised on each spread and a mirror at the end, the littlest of readers will love the surprise each turn of the page holds. This bold and colourful board book is sure to become a fast favourite.

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Book SynopsisOptimal Estimation of Dynamic Systems, Second Edition highlights the importance of both physical and numerical modeling in solving dynamics-based estimation problems found in engineering systems. Accessible to engineering students, applied mathematicians, and practicing engineers, the text presents the central concepts and methods of optimal estimation theory and applies the methods to problems with varying degrees of analytical and numerical difficulty. Different approaches are often compared to show their absolute and relative utility. The authors also offer prototype algorithms to stimulate the development and proper use of efficient computer programs. MATLAB codes for the examples are available on the book's website.New to the Second EditionWith more than 100 pages of new material, this reorganized edition expands upon the best-selling original to include comprehensive developments and updates. It incorporates new Trade ReviewPraise for the First EditionA nice feature of this book is that it makes the effort to explain the underlying principles behind the formula for each algorithm; the relationship between different algorithms is equally well addressed. … The text is a good combination of theory and practice. It will be a valuable addition to references for academic researchers and industrial engineers working in the field of estimation. It will also serve as a useful reference for graduate courses in control and estimation.—AIAA Journal, Vol. 43, No. 1, January 2005Table of ContentsLeast Squares Approximation. Probability Concepts in Least Squares. Sequential State Estimation. Advanced Topics in Sequential State Estimation. Batch State Estimation. Parameter Estimation: Applications. Estimation of Dynamic Systems: Applications. Optimal Control and Estimation Theory. Appendices. Index.

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Book SynopsisThis second, much enlarged edition by Lehmann and Casella of Lehmann's classic text on point estimation maintains the outlook and general style of the first edition. All of the topics are updated, while an entirely new chapter on Bayesian and hierarchical Bayesian approaches is provided, and there is much new material on simultaneous estimation.Trade Review Table of Contents1. Preparations; 2. Unbiasedness; 3. Equivariance; 4. Average risk optimality; 5. Global risk optimality; 6. Asymptotic efficiency and likelihood

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Book SynopsisYou won''t believe your eyes! Laugh in surprise when you find out which animals are hiding behind the flaps... Can you guess?Who's that crunching a juicy carrot? Is it Rabbit? No! It's Parrot.A brilliantly interactive, stylish, young and fun Guess Who board book with big flaps to lift and a mirror ending. With a rhythmic, rhyming text from beloved storyteller Smriti Halls and deliciously vibrant artwork from internationally-renowned artist Marta Altés, nothing is quite what is seems in these bright, bold books full of surprises. With funny animals cleverly disguised on each spread, the littlest of readers will love the reveal each lift-the-flap holds. At the end there''s a mirror to find out who else likes playing. Yes, it''s you!

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Book SynopsisPull a card and try to figure out the historical, fictional, or celebrity names your teammates are clueing you into in this fast-paced guessing game!Split the players into teams of two. One player draws a card and holds it to their forehead without reading it. Their teammate picks one of the five names on the card and tries to help their partner guess it as quickly as possible within one minute. If they get the right name fast, they can continue with the other two names on the card or take a new card and continue to play. After one minute, play switches to the next team. The team with the most correct answers wins!FUN FOR THE WHOLE FAMILY: Celebrity meets Heads Up! in this raucous team game which is perfect for players of all ages! So many names (250 to be exact!) keep the guessing going.   HANDY PORTABLE SIZE: The compact format lets you take the game anywhere! These petite tins are perfect for slipping into a purse or

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Book SynopsisOptimal Estimation of Dynamic Systems, Second Edition highlights the importance of both physical and numerical modeling in solving dynamics-based estimation problems found in engineering systems. Accessible to engineering students, applied mathematicians, and practicing engineers, the text presents the central concepts and methods of optimal estimation theory and applies the methods to problems with varying degrees of analytical and numerical difficulty. Different approaches are often compared to show their absolute and relative utility. The authors also offer prototype algorithms to stimulate the development and proper use of efficient computer programs. MATLAB codes for the examples are available on the bookâs website.New to the Second EditionWith more than 100 pages of new material, this reorganized edition expands upon the best-selling original to include comprehensive developments and updates. It incorporates new theoretical re

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Book SynopsisRenowned philosopher of science David Z Albert offers an innovative approach to understanding the fundamental physical underpinnings of quantum mechanics. Albert shows how we can discern all the baffling features of quantum theory in a simple picture of the pushings and pullings of concrete and high-dimensional, fundamental physical “stuff.”Trade ReviewThe physical interpretation of quantum mechanics has been a controversial riddle since the 1920s, when Niels Bohr argued that the atom’s inner workings could not be described in physical terms. Today, many philosophers and physicists disagree, but there’s no consensus on an alternative. Philosopher David Albert’s provocative book argues, in three essays, that Bohr’s quantum-measurement problem starts to make sense if the wave function is understood as the fundamental physical ‘stuff’ of the Universe. -- Andrew Robinson * Nature *An enormously significant contribution to the philosophy of physics and to metaphysics more generally. In his usual charming and deceptively easy-to-follow style, Albert proposes a novel account of the relation between the fundamental and the non-fundamental—one of the central issues in metaphysics. This is sure to generate a great deal of discussion in the field. -- Barry Loewer, Distinguished Professor of Philosophy, Rutgers UniversityA must-read for anyone interested in the philosophy of physics or adjacent portions of metaphysics. Wave-function realism’s offensive is advanced, its defenses bolstered, its intuitive core reimagined. Insightful and deep and challenging and (of course) fun—vintage Albert. -- Theodore Sider, author of The Tools of Metaphysics and the Metaphysics of ScienceAlbert presents a strikingly original picture of the structure of quantum mechanics and how it describes the world. He shows, by construction, what it is that unifies approaches like the Ghirardi-Rimini-Weber theory, Bohmian mechanics, and the many-worlds formulations. For those who understand the quantum measurement problem and have begun to think carefully about how to solve it, this is an essential read. -- Jeffrey Barrett, author of The Conceptual Foundations of Quantum MechanicsFor a quarter of a century, David Albert has been one of the chief advocates of the wave-function-realist interpretation of quantum mechanics. In this beautifully written and provocative new book, Albert presents the case, as he sees it, for wave-function realism and its surprising higher-dimensional metaphysical framework. -- Alyssa Ney, author of The World in the Wave Function: A Metaphysics for Quantum PhysicsQuantum-mechanical phenomena prove that somehow or other classical physics—and even ‘common sense’—have led us massively astray about the fundamental structure of the world. Albert, in his inimitable conversational style, digs deeply into the argument that our intuitive notion of the structure of physical space lies at the root of the problem. -- Tim Maudlin, author of Philosophy of Physics: Quantum Theory

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Book SynopsisONE ROOM. FIVE SUSPECTS. THREE HOURS TO FIND A KILLER.''An impressive debut'' James Oswald************** GUESS WHO A waitress. A cleaner. An actress. A lawyer. A student. Everyone is a suspect.WHERE In a locked room - with no escape, and no idea how they got there. WHAT In the bathtub, the body of a man they all knew. Someone murdered him. Someone in this room. WHY They have three hours to find out. Or they all die. THE RULES ARE SIMPLE. THE GAME IS NOT Imagine Agatha Christie had created an interactive Escape the Room game, and GUESS WHO would be the result. For fans of the DI Helen Grace series by MJ Arlidge, The Woman in Cabin 10 by Ruth Ware, Ragdoll by Daniel Cole, and Rattle by Fiona Cummins.**************Trade ReviewGuess Who boils with claustrophobic intensity. Packed with gripping twists and turns, Guess Who is an inventive, entertaining locked room mystery that kept me utterly hooked. * Adam Hamdy *An ingenious twisty mystery in a totally unique setting. * Claire McGowan *An impressive debut and a sign of great things to come. * James Oswald *Chris McGeorge's Guess Who is a fresh take on the locked room murder mystery. The plotting is intricate, the characters well drawn, and the pace never lets up as it drives headlong to the surprising end. * David C. Taylor, Edgar-nominated, Nero Wolfe Award-winning author of Night Life *Guess Who is an incredibly satisfying, intelligent thriller running at break-neck pace right until it's closing pages. A marvellous debut. * Francesca Dorricott, author of After the Eclipse *One hotel room. Six strangers. One corpse. Good fun. * Cavan Scott *This is brilliant, so cleverly written and still surprising despite the team's love of escape rooms. * Woman's Way *A FANTASTIC whodunnit with more twists than a Great Hotel corkscrew. * Jenny Blackhurst *

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Book SynopsisThis book examines the consequences of misspecifications from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference.Trade Review'... contains much material of interest to econometricians … a useful source book for researchers, instructors and graduate students and essential reading for those interested in the effects of misspecification.' Econometric TheoryTable of Contents1. Introductory remarks; 2. Probability densities, likelihood functions and the quasi-maximum likelihood estimator; 3. Consistency of the QMLE; 4. Correctly specified models of density; 5. Correctly specified models of conditional expectation; 6. The asymptotic distribution of the QMLE and the information matrix equality; 7. Asymptotic efficiency; 8. Hypothesis testing and asymptotic covariance matrix estimation; 9. Specification testing via m-tests; 10. Applications of m-testing; 11. Information matrix testing; 12. Conclusion; Appendix 1. Elementary concepts of measure theory and the Radon-Nikodym theorem; Appendix 2. Uniform laws of large numbers; Appendix 3. Central limit theorems.

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Book SynopsisAround the world a multitude of surveys are conducted every day, on a variety of subjects, and consequently surveys have become an accepted part of modern life. However, in recent years survey estimates have been increasingly affected by rising trends in nonresponse, with loss of accuracy as an undesirable result.Trade Review"…useful…for the theoretically inclined survey statistician…" (Technometrics, May 2007)"This impressive piece of work can be thoroughly recommended to anyone" (Zentralblatt MATH Volume 1079)Table of ContentsPreface. Chapter 1: Introduction. Chapter 2: The Survey and Its Imperfections. 2.1 The survey objective. 2.2 Sources of error in a survey. Chapter 3: General Principles to Assist Estimation. 3.1 Introduction. 3.2 The importance of auxiliary information. 3.3 Desirable features of an auxiliary vector. Chapter 4: The Use of Auxiliary Information under Ideal Conditions. 4.1 Introduction. 4.2 The Horvitz–Thompson estimator. 4.3 The generalized regression estimator. 4.4 Variance and variance estimation. 4.5 Examples of the generalized regression estimator. Chapter 5: Introduction to Estimation in the Presence of Nonresponse. 5.1 General background. 5.2 Errors caused by sampling and nonresponse. Appendix: Variance and mean squared error under nonresponse. Chapter 6: Weighting of Data in the Presence of Nonresponse. 6.1 Traditional approaches to weighting. 6.2 Auxiliary vectors and auxiliary information. 6.3 The calibration approach: some terminology. 6.4 Point estimation under the calibration approach. 6.5 Calibration estimators for domains. 6.6 Comments on the calibration approach. 6.7 Alternative sets of calibrated weights. 6.8 Properties of the calibrated weights. Chapter 7: Examples of Calibration Estimators. 7.1 Examples of familiar estimators for data with nonresponse. 7.2 The simplest auxiliary vector. 7.3 One-way classification. 7.4 A single quantitative auxiliary variable. 7.5 One-way classification combined with a quantitative variable. 7.6 Two-way classification. 7.7 A Monte Carlo simulation study. Chapter 8 The Combined Use of Sample Information and Population Information. 8.1 Options for the combined use of information. 8.2 An example of calibration with information at both levels. 8.3 A Monte Carlo simulation study of alternative calibration procedures. 8.4 Two-step procedures in practice. Chapter 9 Analysing the Bias due to Nonresponse. 9.1 Simple estimators and their nonresponse bias. 9.2 Finding an efficient grouping. 9.3 Further illustrations of the nonresponse bias. 9.4 A general expression for the bias of the calibration estimator. 9.5 Conditions for near-unbiasedness. 9.6 A review of concepts, terms and ideas. Appendix: Proof of Proposition 9.1. Chapter 10: Selecting the Most Relevant Auxiliary Information. 10.1 Discussion. 10.2 Guidelines for the construction of an auxiliary vector. 10.3 The prospects for near-zero bias with traditional estimators. 10.4 Further avenues towards a zero bias. 10.5 A further tool for reducing the bias. 10.6 The search for a powerful auxiliary vector. 10.7 Empirical illustrations of the indicators. 10.8 Literature review. Chapter 11: Variance and Variance Estimation. 11.1 Variance estimation for the calibration estimator. 11.2 An estimator for ideal conditions. 11.3 A useful relationship. 11.4 Variance estimation for the two-step A and two-step B procedures. 11.5 A simulation study of the variance estimation technique. 11.6 Computational aspects of point and variance estimation. Appendix: Properties of two-phase GREG estimator. Chapter 12: Imputation. 12.1 What is imputation? 12.2 Terminology. 12.3 Multiple study variables. 12.4 The full imputation approach. 12.5 The combined approach. 12.6 The full weighting approach. 12.7 Imputation by statistical rules. 12.8 Imputation by expert judgement or historical data. Chapter 13: Variance Estimation in the Presence of Imputation. 13.1 Issues in variance estimation under the full imputation approach. 13.2 An identity of combined and fully weighted approaches. 13.3 More on the risk of underestimating the variance. 13.4 A broader view of variance estimation for the combined approach. 13.5 Other issues arising with regard to item nonresponse. 13.6 Further comments on imputation. Appendix: Proof of Proposition 13.1. Chapter 14: Estimation Under Nonresponse and Frame Imperfections. 14.1 Introduction. 14.2 Estimation of the persister total. 14.3 Direct estimation of the target population total. 14.4 A case study. References. Index.

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Book SynopsisApplied Likelihood Methods provides an accessible and practical introduction to likelihood modeling, supported by examples and software. The book features applications from a range of disciplines, including statistics, medicine, biology, and ecology.Trade Review“This book is well-presented and would suit applied scientists, researchers, graduate students and particularly anyone who uses likelihood and such methods to their studies and applications.” (ISR, 2012) Table of ContentsPreface xiii Part I PRELIMINARIES 1 1 A taste of likelihood 3 1.1 Introduction 3 1.2 Motivating example 4 1.3 Using SAS, R and ADMB 9 1.4 Implementation of the motivating example 11 1.5 Exercises 17 2 Essential concepts and iid examples 18 2.1 Introduction 18 2.2 Some necessary notation 19 2.3 Interpretation of likelihood 23 2.4 IID examples 25 2.5 Exercises 33 Part II PRAGMATICS 37 3 Hypothesis tests and confidence intervals or regions 39 3.1 Introduction 39 3.2 Approximate normality of MLEs 40 3.3 Wald tests, confidence intervals and regions 43 3.4 Likelihood ratio tests, confidence intervals and regions 49 3.5 Likelihood ratio examples 54 3.6 Profile likelihood 57 3.7 Exercises 59 4 What you really need to know 64 4.1 Introduction 64 4.2 Inference about g(θ) 65 4.3 Wald statistics – quick and dirty? 75 4.4 Model selection 79 4.5 Bootstrapping 81 4.6 Prediction 91 4.7 Things that can mess you up 95 4.8 Exercises 98 5 Maximizing the likelihood 101 5.1 Introduction 101 5.2 The Newton-Raphson algorithm 103 5.3 The EM (Expectation–Maximization) algorithm 104 5.4 Multi-stage maximization 113 5.5 Exercises 118 6 Some widely used applications of maximum likelihood 121 6.1 Introduction 121 6.2 Box-Cox transformations 122 6.3 Models for survival-time data 125 6.4 Mark–recapture models 134 6.5 Exercises 141 7 Generalized linear models and extensions 143 7.1 Introduction 143 7.2 Specification of a GLM 144 7.3 Likelihood calculations 148 7.4 Model evaluation 149 7.5 Case study 1: Logistic regression and inverse prediction in R 154 7.6 Beyond binomial and Poisson models 161 7.7 Case study 2: Multiplicative vs additive models of over-dispersed counts in SAS 167 7.8 Exercises 173 8 Quasi-likelihood and generalized estimating equations 175 8.1 Introduction 175 8.2 Wedderburn’s quasi-likelihood 177 8.3 Generalized estimating equations 181 8.4 Exercises 187 9 ML inference in the presence of incidental parameters 188 9.1 Introduction 188 9.2 Conditional likelihood 192 9.3 Integrated likelihood 198 9.3.1 Justification 199 9.3.2 Uses of integrated likelihood 200 9.4 Exercises 201 10 Latent variable models 202 10.1 Introduction 202 10.2 Developing the likelihood 203 10.3 Software 204 10.4 One-way linear random-effects model 210 10.5 Nonlinear mixed-effects model 217 10.6 Generalized linear mixed-effects model 221 10.7 State-space model for count data 227 10.8 ADMB template files 228 10.9 Exercises 232 Part III THEORETICAL FOUNDATIONS 233 11 Cramer-Rao inequality and Fisher information 235 11.1 Introduction 235 11.2 The Cramer-Rao inequality for θ RI 236 11.3 Cramer-Rao inequality for functions of θ 239 11.4 Alternative formulae for I (θ) 241 11.5 The iid data case 243 11.6 The multi-dimensional case, θ RI s 243 11.7 Examples of Fisher information calculation 247 11.8 Exercises 253 12 Asymptotic theory and approximate normality 256 12.1 Introduction 256 12.2 Consistency and asymptotic normality 257 12.3 Approximate normality 271 12.4 Wald tests and confidence regions 276 12.5 Likelihood ratio test statistic 280 12.6 Rao-score test statistic 281 12.7 Exercises 283 13 Tools of the trade 286 13.1 Introduction 286 13.2 Equivalence of tests and confidence intervals 286 13.3 Transformation of variables 287 13.4 Mean and variance conditional identities 288 13.5 Relevant inequalities 289 13.6 Asymptotic probability theory 291 13.7 Exercises 297 14 Fundamental paradigms and principles of inference 299 14.1 Introduction 299 14.2 Sufficiency principle 300 14.3 Conditionality principle 304 14.4 The likelihood principle 306 14.5 Statistical significance versus statistical evidence 309 14.6 Exercises 311 15 Miscellanea 313 15.1 Notation 313 15.2 Acronyms 315 15.3 Do you think like a frequentist or a Bayesian? 315 15.4 Some useful distributions 316 15.5 Software extras 321 15.6 Automatic differentiation 323 Appendix: Partial solutions to selected exercises 325 Bibliography 337 Index 345

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Book SynopsisGeneralized Estimating Equations, Second Edition updates the best-selling previous edition, which has been the standard text on the subject since it was published a decade ago. Combining theory and application, the text provides readers with a comprehensive discussion of GEE and related models. Numerous examples are employed throughout the text, along with the software code used to create, run, and evaluate the models being examined. Stata is used as the primary software for running and displaying modeling output; associated R code is also given to allow R users to replicate Stata examples. Specific examples of SAS usage are provided in the final chapter as well as on the book's website.This second edition incorporates comments and suggestions from a variety of sources, including the Statistics.com course on longitudinal and panel models taught by the authors. Other enhancements include an examination of GEE marginal effects; a more thorough presentatiTrade Review"Overall, I found this to be a very useful book on GEE, and would recommend it to anyone planning to use GEE models in their data analysis. Both the theory and practical aspects of constructing and analysing such models is covered. Inclusion of code for many of the analyses is an excellent feature."—Ken J. Beath, Macquarie University, Australia, Australian and New Zealand Journal of Statistics, April 2017" … the authors expand the text with several additions: (I) they examine and include entirely new topics related to GEE and the estimation of clustered and longitudinal models; (2) they add more detailed discussions of previously presented topics, including expanding the discussion of various models associated with GEE (penalized GEE, survey GEE, and quasi-least-square regression), adding material on hypothesis testing and diagnostics, and introducing alternative models for ordered categorical outcomes and an extension of the QIC, which is a model selection criterion measure; (3) they expand the amount of computer code by adding R code to duplicate the Stata examples wherever possible. In my opinion, the second edition is enhanced by the additions mentioned above, providing an excellent review of the GEE, wide coverage of its variations, and many useful computing techniques. I believe it would be a very useful reference book for practicing researchers and graduate students who are interested in research topics related to GEE."—CindyYu, Iowa State University in the Journal of the American Statistical Association, December 2013"The second edition … adds a few new topics related to various extensions of GEE … [and replaces] outdated S-PLUS codes with R scripts. Also, the number of exercises increased significantly … . For those who want to use this book in the classroom, including me, having extra exercise sets is certainly a welcome addition. … One main strength of this book is its comprehensive coverage of Stata implementation of the GEE. … a valuable reference and is particularly useful for practitioners. It can serve as supplemental reading in longitudinal data analysis classes as well."—Woncheol Jang, Biometrics, September 2013Praise for the First Edition:"… well-written chapters … . The book contains challenging problems in exercises and is suitable to be a textbook in a graduate-level course on estimating functions. The references are up-to-date and exhaustive. … I enjoyed reading [this book] and recommend [it] very highly to the statistical community."—Journal of Statistical Computation and Simulation, February 2005"[The book] is comprehensive and covers much useful material with formulas presented in detail … a useful and recommendable book both for those who already work with GEE methods and for newcomers to the field."—Per Kragh Andersen, University of Copenhagen, Statistics in Medicine, 2004"Generalized Estimating Equations is the first and only book to date dedicated exclusively to generalized estimating equations (GEE). I find it to be a good reference text for anyone using generalized linear models (GLIM).The authors do a good job of not only presenting the general theory of GEE models, but also giving explicit examples of various correlation structures, link functions and a comparison between population-averaged and subject-specific models. Furthermore, there are sections on the analysis of residuals, deletion diagnostics, goodness-of-fit criteria, and hypothesis testing. Good data-driven examples that give comparisons between different GEE models are provided throughout the book. Perhaps the greatest strength of this book is its completeness. It is a thorough compendium of information from the GEE literature. Overall, Generalized Estimating Equations contains a unique survey of GEE models in an attempt to unify notation and provide the most in-depth treatment of GEEs. I believe that it serves as a valuable reference for researchers, teachers, and students who study and practice GLIM methodology."—Journal of the American Statistics Association, March 2004"Generalized Estimating Equations is a good introductory book for analysing continuous and discrete data using GEE methods ... . This book is easy to read, and it assumes that the reader has some background in GLM. Many examples are drawn from biomedical studies and survey studies, and so it provides good guidance for analysing correlated data in these and other areas."—Technometrics, 2003"Overall, I found this to be a very useful book on GEE, and would recommend it to anyone planning to use GEE models in their data analysis. Both the theory and practical aspects of constructing and analysing such models is covered. Inclusion of code for many of the analyses is an excellent feature."—Ken J. Beath, Macquarie University, Australia, Australian and New Zealand Journal of Statistics, April 2017"The second edition … adds a few new topics related to various extensions of GEE … [and replaces] outdated S-PLUS codes with R scripts. Also, the number of exercises increased significantly … . For those who want to use this book in the classroom, including me, having extra exercise sets is certainly a welcome addition. … One main strength of this book is its comprehensive coverage of Stata implementation of the GEE. … a valuable reference and is particularly useful for practitioners. It can serve as supplemental reading in longitudinal data analysis classes as well."—Woncheol Jang, Biometrics, September 2013Praise for the First Edition:"… well-written chapters … . The book contains challenging problems in exercises and is suitable to be a textbook in a graduate-level course on estimating functions. The references are up-to-date and exhaustive. … I enjoyed reading [this book] and recommend [it] very highly to the statistical community."—Journal of Statistical Computation and Simulation, February 2005"[The book] is comprehensive and covers much useful material with formulas presented in detail … a useful and recommendable book both for those who already work with GEE methods and for newcomers to the field."—Per Kragh Andersen, University of Copenhagen, Statistics in Medicine, 2004"Generalized Estimating Equations is the first and only book to date dedicated exclusively to generalized estimating equations (GEE). I find it to be a good reference text for anyone using generalized linear models (GLIM).The authors do a good job of not only presenting the general theory of GEE models, but also giving explicit examples of various correlation structures, link functions and a comparison between population-averaged and subject-specific models. Furthermore, there are sections on the analysis of residuals, deletion diagnostics, goodness-of-fit criteria, and hypothesis testing. Good data-driven examples that give comparisons between different GEE models are provided throughout the book. Perhaps the greatest strength of this book is its completeness. It is a thorough compendium of information from the GEE literature. Overall, Generalized Estimating Equations contains a unique survey of GEE models in an attempt to unify notation and provide the most in-depth treatment of GEEs. I believe that it serves as a valuable reference for researchers, teachers, and students who study and practice GLIM methodology."—Journal of the American Statistics Association, March 2004"Generalized Estimating Equations is a good introductory book for analysing continuous and discrete data using GEE methods ... . This book is easy to read, and it assumes that the reader has some background in GLM. Many examples are drawn from biomedical studies and survey studies, and so it provides good guidance for analysing correlated data in these and other areas."—Technometrics, 2003Table of ContentsIntroduction. Model Construction and Estimating Equations. Generalized Estimating Equations. Residuals, Diagnostics, and Testing. Programs and Datasets. References. Author Index. Subject Index.

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Book SynopsisFamiliar flowers become surprising creatures with the help of lively lift-the-flaps Blossoms turn into animals with the lift of a flap in this fun board book. Young readers are encouraged to use their imaginations to guess what animal will be revealed. A tulip becomes a yellow cat; a white daisy turns into a sheep. What will become of a cherry blossom, a dandelion, and a pansy? Children will enjoy brainstorming to guess each surprise.

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Book SynopsisGet your inner doggy on with this fun and whacky game! The rules are simple in this game for 2, but the process of elimination is dastardly!Each player gets a tray and a pack of cards. They display their cards in the slots on the tray and the youngest player begins … ‘Is your dog wearing a collar?’ If the answer is ‘yes’, the player asking removes all the cards in front of them that do NOT have collars; if the answer is ‘no’, they remove all the cards that DO have collars. The more interesting the questions the more fun you’ll have with the wonderfully inventive and madcap range of canine characters found here - from Fresh Prince to Indiana Bones, Tootsie to Sir Barks-a-lot, these doggies will make you smile and test your skills as you try to win the game and guess that pooch!

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Book SynopsisYou won''t believe your eyes! Laugh in surprise when you find out which animals are hiding behind the flaps... Can you guess?Who's that hiding behind the chair? Is it Monkey? No! It's Bear.A brilliantly interactive, stylish, young and fun Guess Who board book with big flaps to lift and a mirror ending. With a rhythmic, rhyming text from beloved storyteller Smriti Halls and deliciously vibrant artwork from internationally-renowned artist Marta Altés, nothing is quite what is seems in these bright, bold books full of surprises. With funny animals cleverly disguised on each spread, the littlest of readers will love the reveal each lift-the-flap holds. At the end there''s a mirror to find out who else likes playing. Yes, it''s you!

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Book SynopsisFeatures of this updated edition include:99 oversized clue cards with 15 clues questions eachAll-new Person, Place and Thing artA title reference on each game card connects to The Action Bible Expanded EditionGame instructions with several ways to playPerfect for traveling, family game night, or the classroomMakes learning about the Bible fun and intriguingExpands Bible knowledgeAdaptable for multiple age levelsFor 1 or more players; ages 6+.

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Book SynopsisOur book introduces a method to evaluate the accuracy of trend estimation algorithms under conditions similar to those encountered in real time series processing. This method is based on Monte Carlo experiments with artificial time series numerically generated by an original algorithm. The second part of the book contains several automatic algorithms for trend estimation and time series partitioning. The source codes of the computer programs implementing these original automatic algorithms are given in the appendix and will be freely available on the web. The book contains clear statement of the conditions and the approximations under which the algorithms work, as well as the proper interpretation of their results. We illustrate the functioning of the analyzed algorithms by processing time series from astrophysics, finance, biophysics, and paleoclimatology. The numerical experiment method extensively used in our book is already in common use in computational and statistical physics.Table of ContentsDiscrete stochastic processes and time series.- Trend definition.- Finite AR(1) stochastic process.- Monte Carlo experiments. - Monte Carlo statistical ensembles.- Numerical generation of trends.- Numerical generation of noisy time series.- Statistical hypothesis testing.- Testing the i.i.d. property.- Polynomial fitting.- Linear regression.- Polynomial fitting.- Polynomial fitting of artificial time series.- An astrophysical example.- Noise smoothing.- Moving average.- Repeated moving average (RMA).- Smoothing of artificial time series.- A financial example.- Automatic estimation of monotonic trends.- Average conditional displacement (ACD) algorithm.- Artificial time series with monotonic trends.- Automatic ACD algorithm.- Evaluation of the ACD algorithm.- A paleoclimatological example.- Statistical significance of the ACD trend.- Time series partitioning.- Partitioning of trends into monotonic segments.- Partitioning of noisy signals into monotonic segments.- Partitioning of a real time series.- Estimation of the ratio between the trend and noise.- Automatic estimation of arbitrary trends.- Automatic RMA (AutRMA).- Monotonic segments of the AutRMA trend.- Partitioning of a financial time series.

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Book SynopsisThis title focuses on two significant problems in the field of automatic control, in particular state estimation and robust Model Predictive Control under input and state constraints, bounded disturbances and measurement noises. The authors build upon previous results concerning zonotopic set-membership state estimation and output feedback tube-based Model Predictive Control. Various existing zonotopic set-membership estimation methods are investigated and their advantages and drawbacks are discussed, making this book suitable both for researchers working in automatic control and industrial partners interested in applying the proposed techniques to real systems. The authors proceed to focus on a new method based on the minimization of the P-radius of a zonotope, in order to obtain a good trade-off between the complexity and the accuracy of the estimation. They propose a P-radius based set-membership estimation method to compute a zonotope containing the real states of a system, which are consistent with the disturbances and measurement noise. The problem of output feedback control using a zonotopic set-membership estimation is also explored. Among the approaches from existing literature on the subject, the implementation of robust predictive techniques based on tubes of trajectories is developed. Contents 1. Uncertainty Representation Based on Set Theory. 2. Several Approaches on Zonotopic Guaranteed Set-Membership Estimation. 3. Zonotopic Guaranteed State Estimation Based on P-Radius Minimization. 4. Tube Model Predictive Control Based on Zonotopic Set-Membership Estimation. About the Authors Vu Tuan Hieu Le is a Research Engineer at the IRSEEM/ESIGELEC Technopôle du Madrillet, Saint Etienne du Rouvray, France. Cristina Stoica is Assistant Professor in the Automatic Control Department at SUPELEC Systems Sciences (E3S), France. Teodoro Alamo is Professor in the Department of Systems Engineering and Automatic Control at the University of Seville, Spain. Eduardo F. Camacho is Professor in the Department of Systems Engineering and Automatic Control at the University of Seville, Spain. Didier Dumur is Professor in the Automatic Control Department, SUPELEC Systems Sciences (E3S), France.Table of ContentsNotations vii Acronyms ix Introduction xi Chapter 1 Uncertainty Representation Based on Set Theory 1 1.1 Basic set definitions: advantages and weaknesses 3 1.1.1 Interval set 5 1.1.2 Ellipsoidal set 7 1.1.3 Polyhedral set 9 1.1.4 Zonotopic set 12 1.2 Main properties of zonotopes 17 Chapter 2 Several Approaches on Zonotopic Guaranteed Set-Membership Estimation 27 2.1 Context 27 2.2 Problem formulation 32 2.2.1 Singular Value Decomposition-based method 35 2.2.2 Optimization-based methods 40 vi Zonotopes Chapter 3 Zonotopic Guaranteed State Estimation Based on P-Radius Minimization 49 3.1 Single-Output systems approach 49 3.2 Multi-Output systems approaches 63 3.2.1 General formulation 64 3.2.2 Extensions of the Single-Output systems methodology 67 3.2.3 Dedicated approach for Multi-Output systems 85 Chapter 4 Tube Model Predictive Control Based on Zonotopic Set-Membership Estimation 95 4.1 Context 95 4.2 Problem formulation 100 4.3 Tube-based output feedback Model Predictive Control design 100 4.4 Application on the magnetic levitation system 112 4.4.1 System description 113 4.4.2 Control problem 116 Conclusion and Perspectives 125 Appendix Basic Matrix Operation Definitions 129 Bibliography 133 Index 149

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Book SynopsisThe respected classic, now in a handy paperback edition Originally published in 1971, Harry Van Trees'' Detection, Estimation, and Modulation Theory, Part II is one of the classic references in the area of nonlinear modulation theory and analog communication. Highly readable and well organized, it is as valuable today for professionals, researchers, and students interested in the estimation of continuous waveforms as it was over thirty years ago. Part II focuses on the problem of finding the optimum estimate of a waveform which is embedded in a signal in a nonlinear manner. The following topics are covered in detail: Bayesian Cramér-Rao bound on the mean-square estimation error Optimum demodulators for frequency-modulation systems Phase estimation: the synchronization problem Fokker-Planck techniques for nonlinear analysis in the presence of noise Optimum angle-modulation systems Rate distortion boTable of ContentsIntroduction. Optimum Demodulators. Phase Estimation: The Synchronization Problem. Frequency Modulation. Optimum Angle-Modulation Systems. Comparison of Transmission Systems. Nonlinear Estimation Theory: Alternative Approaches. Analog Communication over Randomly Time-varying Channels. Discussion. Appendix: State Variable Analysis Procedures. Glossary. Author Index. Subject Index.

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Book SynopsisPraise for the First Edition This pioneering work, in which Rao provides a comprehensive and up-to-date treatment of small area estimation, will become a classic...I believe that it has the potential to turn small area estimation...into a larger area of importance to both researchers and practitioners.Journal of the American Statistical Association Written by two experts in the field, Small Area Estimation, Second Edition provides a comprehensive and up-to-date account of the methods and theory of small area estimation (SAE), particularly indirect estimation based on explicit small area linking models. The model-based approach to small area estimation offers several advantages including increased precision, the derivation of optimal estimates and associated measures of variability under an assumed model, and the validation of models from the sample data. Emphasizing real data throughout, the Second Edition maintaiTrade Review"The book is an excellent reference for practicing statisticians and survey methodologists as well as practitioners interested in learning SAE methods. The second edition is also an ideal textbook for graduate-level courses in SAE and reliable small area statistics." (Zentralblatt MATH, 2016)Table of ContentsList of Figures xv List of Tables xvii Foreword to the First Edition xix Preface to the Second Edition xxiii Preface to the First Edition xxvii 1 *Introduction 1 1.1 What is a Small Area? 1 1.2 Demand for Small Area Statistics 3 1.3 Traditional Indirect Estimators 4 1.4 Small Area Models 4 1.5 Model-Based Estimation 5 1.6 Some Examples 6 1.6.1 Health 6 1.6.2 Agriculture 7 1.6.3 Income for Small Places 8 1.6.4 Poverty Counts 8 1.6.5 Median Income of Four-Person Families 8 1.6.6 Poverty Mapping 8 2 Direct Domain Estimation 9 2.1 Introduction 9 2.2 Design-Based Approach 10 2.3 Estimation of Totals 11 2.3.1 Design-Unbiased Estimator 11 2.3.2 Generalized Regression Estimator 13 2.4 Domain Estimation 16 2.4.1 Case of No Auxiliary Information 16 2.4.2 GREG Domain Estimation 17 2.4.3 Domain-Specific Auxiliary Information 18 2.5 Modified GREG Estimator 21 2.6 Design Issues 23 2.6.1 Minimization of Clustering 24 2.6.2 Stratification 24 2.6.3 Sample Allocation 24 2.6.4 Integration of Surveys 25 2.6.5 Dual-Frame Surveys 25 2.6.6 Repeated Surveys 26 2.7 *Optimal Sample Allocation for Planned Domains 26 2.7.1 Case (i) 26 2.7.2 Case (ii) 29 2.7.3 Two-Way Stratification: Balanced Sampling 31 2.8 Proofs 32 2.8.1 Proof of ŶGR(𝐱) = 𝐗 32 2.8.2 Derivation of Calibration Weights 𝑤∗j 32 2.8.3 Proof of Y = X^T𝐁^when cj = 𝝂T𝐗j 32 3 Indirect Domain Estimation 35 3.1 Introduction 35 3.2 Synthetic Estimation 36 3.2.1 No Auxiliary Information 36 3.2.2 *Area Level Auxiliary Information 36 3.2.3 *Unit Level Auxiliary Information 37 3.2.4 Regression-Adjusted Synthetic Estimator 42 3.2.5 Estimation of MSE 43 3.2.6 Structure Preserving Estimation 45 3.2.7 *Generalized SPREE 49 3.2.8 *Weight-Sharing Methods 53 3.3 Composite Estimation 57 3.3.1 Optimal Estimator 57 3.3.2 Sample-Size-Dependent Estimators 59 3.4 James–Stein Method 63 3.4.1 Common Weight 63 3.4.2 Equal Variances 𝜓i = 𝜓 64 3.4.3 Estimation of Component MSE 68 3.4.4 Unequal Variances 𝜓i 70 3.4.5 Extensions 71 3.5 Proofs 71 4 Small Area Models 75 4.1 Introduction 75 4.2 Basic Area Level Model 76 4.3 Basic Unit Level Model 78 4.4 Extensions: Area Level Models 81 4.4.1 Multivariate Fay–Herriot Model 81 4.4.2 Model with Correlated Sampling Errors 82 4.4.3 Time Series and Cross-Sectional Models 83 4.4.4 *Spatial Models 86 4.4.5 Two-Fold Subarea Level Models 88 4.5 Extensions: Unit Level Models 88 4.5.1 Multivariate Nested Error Regression Model 88 4.5.2 Two-Fold Nested Error Regression Model 89 4.5.3 Two-Level Model 90 4.5.4 General Linear Mixed Model 91 4.6 Generalized Linear Mixed Models 92 4.6.1 Logistic Mixed Models 92 4.6.2 *Models for Multinomial Counts 93 4.6.3 Models for Mortality and Disease Rates 93 4.6.4 Natural Exponential Family Models 94 4.6.5 *Semi-parametric Mixed Models 95 5 Empirical Best Linear Unbiased Prediction (EBLUP): Theory 97 5.1 Introduction 97 5.2 General Linear Mixed Model 98 5.2.1 BLUP Estimator 98 5.2.2 MSE of BLUP 100 5.2.3 EBLUP Estimator 101 5.2.4 ML and REML Estimators 102 5.2.5 MSE of EBLUP 105 5.2.6 Estimation of MSE of EBLUP 106 5.3 Block Diagonal Covariance Structure 108 5.3.1 EBLUP Estimator 108 5.3.2 Estimation of MSE 109 5.3.3 Extension to Multidimensional Area Parameters 110 5.4 *Model Identification and Checking 111 5.4.1 Variable Selection 111 5.4.2 Model Diagnostics 114 5.5 *Software 118 5.6 Proofs 119 5.6.1 Derivation of BLUP 119 5.6.2 Equivalence of BLUP and Best Predictor E(𝐦T𝐯|𝐀T𝐲) 120 5.6.3 Derivation of MSE Decomposition (5.2.29) 121 6 Empirical Best Linear Unbiased Prediction (EBLUP): Basic Area Level Model 123 6.1 EBLUP Estimation 123 6.1.1 BLUP Estimator 124 6.1.2 Estimation of 𝜎2𝑣 126 6.1.3 Relative Efficiency of Estimators of 𝜎2𝑣 128 6.1.4 *Applications 129 6.2 MSE Estimation 136 6.2.1 Unconditional MSE of EBLUP 136 6.2.2 MSE for Nonsampled Areas 139 6.2.3 *MSE Estimation for Small Area Means 140 6.2.4 *Bootstrap MSE Estimation 141 6.2.5 *MSE of a Weighted Estimator 143 6.2.6 Mean Cross Product Error of Two Estimators 144 6.2.7 *Conditional MSE 144 6.3 *Robust Estimation in the Presence of Outliers 146 6.4 *Practical Issues 148 6.4.1 Unknown Sampling Error Variances 148 6.4.2 Strictly Positive Estimators of 𝜎2𝑣 151 6.4.3 Preliminary Test Estimation 154 6.4.4 Covariates Subject to Sampling Errors 156 6.4.5 Big Data Covariates 159 6.4.6 Benchmarking Methods 159 6.4.7 Misspecified Linking Model 165 6.5 *Software 169 7 Basic Unit Level Model 173 7.1 EBLUP Estimation 173 7.1.1 BLUP Estimator 174 7.1.2 Estimation of 𝜎2𝑣 and 𝜎2e 177 7.1.3 *Nonnegligible Sampling Fractions 178 7.2 MSE Estimation 179 7.2.1 Unconditional MSE of EBLUP 179 7.2.2 Unconditional MSE Estimators 181 7.2.3 *MSE Estimation: Nonnegligible Sampling Fractions 182 7.2.4 *Bootstrap MSE Estimation 183 7.3 *Applications 186 7.4 *Outlier Robust EBLUP Estimation 193 7.4.1 Estimation of Area Means 193 7.4.2 MSE Estimation 198 7.4.3 Simulation Results 199 7.5 *M-Quantile Regression 200 7.6 *Practical Issues 205 7.6.1 Unknown Heteroscedastic Error Variances 205 7.6.2 Pseudo-EBLUP Estimation 206 7.6.3 Informative Sampling 211 7.6.4 Measurement Error in Area-Level Covariate 216 7.6.5 Model Misspecification 218 7.6.6 Semi-parametric Nested Error Model: EBLUP 220 7.6.7 Semi-parametric Nested Error Model: REBLUP 224 7.7 *Software 227 7.8 *Proofs 231 7.8.1 Derivation of (7.6.17) 231 7.8.2 Proof of (7.6.20) 232 8 EBLUP: Extensions 235 8.1 *Multivariate Fay–Herriot Model 235 8.2 Correlated Sampling Errors 237 8.3 Time Series and Cross-Sectional Models 240 8.3.1 *Rao–Yu Model 240 8.3.2 State-Space Models 243 8.4 *Spatial Models 248 8.5 *Two-Fold Subarea Level Models 251 8.6 *Multivariate Nested Error Regression Model 253 8.7 Two-Fold Nested Error Regression Model 254 8.8 *Two-Level Model 259 8.9 *Models for Multinomial Counts 261 8.10 *EBLUP for Vectors of Area Proportions 262 8.11 *Software 264 9 Empirical Bayes (EB) Method 269 9.1 Introduction 269 9.2 Basic Area Level Model 270 9.2.1 EB Estimator 271 9.2.2 MSE Estimation 273 9.2.3 Approximation to Posterior Variance 275 9.2.4 *EB Confidence Intervals 281 9.3 Linear Mixed Models 287 9.3.1 EB Estimation of 𝜇i = 𝐥iT𝜷 + 𝐦Ti 𝐯i 287 9.3.2 MSE Estimation 288 9.3.3 Approximations to the Posterior Variance 288 9.4 *EB Estimation of General Finite Population Parameters 289 9.4.1 BP Estimator Under a Finite Population 290 9.4.2 EB Estimation Under the Basic Unit Level Model 290 9.4.3 FGT Poverty Measures 293 9.4.4 Parametric Bootstrap for MSE Estimation 294 9.4.5 ELL Estimation 295 9.4.6 Simulation Experiments 296 9.5 Binary Data 298 9.5.1 *Case of No Covariates 299 9.5.2 Models with Covariates 304 9.6 Disease Mapping 308 9.6.1 Poisson–Gamma Model 309 9.6.2 Log-Normal Models 310 9.6.3 Extensions 312 9.7 *Design-Weighted EB Estimation: Exponential Family Models 313 9.8 Triple-Goal Estimation 315 9.8.1 Constrained EB 316 9.8.2 Histogram 318 9.8.3 Ranks 318 9.9 Empirical Linear Bayes 319 9.9.1 LB Estimation 319 9.9.2 Posterior Linearity 322 9.10 Constrained LB 324 9.11 *Software 325 9.12 Proofs 330 9.12.1 Proof of (9.2.11) 330 9.12.2 Proof of (9.2.30) 330 9.12.3 Proof of (9.8.6) 331 9.12.4 Proof of (9.9.1) 331 10 Hierarchical Bayes (HB) Method 333 10.1 Introduction 333 10.2 MCMC Methods 335 10.2.1 Markov Chain 335 10.2.2 Gibbs Sampler 336 10.2.3 M–H Within Gibbs 336 10.2.4 Posterior Quantities 337 10.2.5 Practical Issues 339 10.2.6 Model Determination 342 10.3 Basic Area Level Model 347 10.3.1 Known 𝜎2𝑣 347 10.3.2 *Unknown 𝜎2𝑣: Numerical Integration 348 10.3.3 Unknown 𝜎2𝑣: Gibbs Sampling 351 10.3.4 *Unknown Sampling Variances 𝜓i 354 10.3.5 *Spatial Model 355 10.4 *Unmatched Sampling and Linking Area Level Models 356 10.5 Basic Unit Level Model 362 10.5.1 Known 𝜎2𝑣 and 𝜎2e 362 10.5.2 Unknown 𝜎2𝑣 and 𝜎2e: Numerical Integration 363 10.5.3 Unknown 𝜎2𝑣 and 𝜎2e: Gibbs Sampling 364 10.5.4 Pseudo-HB Estimation 365 10.6 General ANOVA Model 368 10.7 *HB Estimation of General Finite Population Parameters 369 10.7.1 HB Estimator under a Finite Population 370 10.7.2 Reparameterized Basic Unit Level Model 370 10.7.3 HB Estimator of a General Area Parameter 372 10.8 Two-Level Models 374 10.9 Time Series and Cross-Sectional Models 377 10.10 Multivariate Models 381 10.10.1 Area Level Model 381 10.10.2 Unit Level Model 382 10.11 Disease Mapping Models 383 10.11.1 Poisson-Gamma Model 383 10.11.2 Log-Normal Model 384 10.11.3 Two-Level Models 386 10.12 *Two-Part Nested Error Model 388 10.13 Binary Data 389 10.13.1 Beta-Binomial Model 389 10.13.2 Logit-Normal Model 390 10.13.3 Logistic Linear Mixed Models 393 10.14 *Missing Binary Data 397 10.15 Natural Exponential Family Models 398 10.16 Constrained HB 399 10.17 *Approximate HB Inference and Data Cloning 400 10.18 Proofs 402 10.18.1 Proof of (10.2.26) 402 10.18.2 Proof of (10.2.32) 402 10.18.3 Proof of (10.3.13)–(10.3.15) 402 References 405 Author Index 431 Subject Index 437

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Book SynopsisThis book offers solutions to the problems commonly encountered by economists trying to squeeze information out of partial or incomplete data--which is usually what they have to work with.Table of ContentsThe Classical Maximum Entropy Formalism: A Review. PURE INVERSE PROBLEMS. Basic Maximum Entropy Principle: Formulation and Extensions. Formulation and Solution of Pure Inverse Problems. Generalized Pure Inverse Problems. LINEAR INVERSE PROBLEMS WITH NOISE. Generalized Maximum Entropy (GME) and Cross-Entropy (GCE)Formulations. Finite Sample Extensions of GME-GCE. GENERAL LINEAR MODEL APPLICATIONS OF GME-GCE. GME-GCE Solutions to Ill-conditioned Problems. General Linear Statistical Model with a Non-scalar IdentityCovariance Matrix Statistical Model Selection. A SYSTEM OF ECONOMIC STATISTICAL RELATIONS. Sets of Linear Statistical Models. Simultaneous Equations Statistical Model. LINEAR AND NON-LINEAR DYNAMIC SYSTEMS. Estimation and Inference of Dynamic Linear Inverse Problems. Linear and Non-linear Dynamic Systems with Control. DISCRETE CHOICE-CENSORED PROBLEMS. Recovering Information from Multinomial Response Data. Recovering Information from Censored Response Data. COMPUTATIONAL NOTES. Computing GME-GCE Solutions. Epilogue. Selected Reading. Index.

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Book SynopsisThis book introduces theoretical concepts to explain the fundamentals of the design and evaluation of software estimation models. It provides software professionals with vital information on the best software management software out there. End-of-chapter exercises Over 100 figures illustrating the concepts presented throughout the book Examples incorporated with industry data Table of ContentsForeword xiii Overview xvii Acknowledgments xxiii About the Author xxv Part One Understanding the Estimation Process 1 1. The Estimation Process: Phases and Roles 3 1.1. Introduction 3 1.2. Generic Approaches in Estimation Models: Judgment or Engineering? 4 1.2.1. Practitioner’s Approach: Judgment and Craftsmanship 4 1.2.2. Engineering Approach: Modest–One Variable at a Time 5 1.3. Overview of Software Project Estimation and Current Practices 6 1.3.1. Overview of an Estimation Process 6 1.3.2. Poor Estimation Practices 7 1.3.3. Examples of Poor Estimation Practices 9 1.3.4. The Reality: A Tally of Failures 10 1.4. Levels of Uncertainty in an Estimation Process 11 1.4.1. The Cone of Uncertainty 11 1.4.2. Uncertainty in a Productivity Model 12 1.5. Productivity Models 14 1.6. The Estimation Process 16 1.6.1. The Context of the Estimation Process 16 1.6.2. The Foundation: The Productivity Model 17 1.6.3. The Full Estimation Process 18 1.7. Budgeting and Estimating: Roles and Responsibilities 23 1.7.1. Project Budgeting: Levels of Responsibility 23 1.7.2. The Estimator 25 1.7.3. The Manager (Decision-Taker and Overseer) 25 1.8. Pricing Strategies 27 1.8.1. Customers-Suppliers: The Risk Transfer Game in Estimation 28 1.9. Summary – Estimating Process, Roles, and Responsibilities 28 Exercises 30 Term Assignments 31 2. Engineering and Economics Concepts for Understanding Software Process Performance 32 2.1. Introduction: The Production (Development) Process 32 2.2. The Engineering (and Management) Perspective on a Production Process 34 2.3. Simple Quantitative Process Models 36 2.3.1. Productivity Ratio 36 2.3.2. Unit Effort (or Unit Cost) Ratio 38 2.3.3. Averages 39 2.3.4. Linear and Non-Linear Models 42 2.4. Quantitative Models and Economics Concepts 45 2.4.1. Fixed and Variable Costs 45 2.4.2. Economies and Diseconomies of Scale 48 2.5. Software Engineering Datasets and Their Distribution 49 2.5.1. Wedge-Shaped Datasets 49 2.5.2. Homogeneous Datasets 50 2.6. Productivity Models: Explicit and Implicit Variables 52 2.7. A Single and Universal Catch-All Multidimensional Model or Multiple Simpler Models? 54 2.7.1. Models Built from Available Data 55 2.7.2. Models Built on Opinions on Cost Drivers 55 2.7.3. Multiple Models with Coexisting Economies and Diseconomies of Scale 56 Exercises 58 Term Assignments 59 3. Project Scenarios, Budgeting, and Contingency Planning 60 3.1. Introduction 60 3.2. Project Scenarios for Estimation Purposes 61 3.3. Probability of Underestimation and Contingency Funds 65 3.4. A Contingency Example for a Single Project 67 3.5. Managing Contingency Funds at the Portfolio Level 69 3.6. Managerial Prerogatives: An Example in the AGILE Context 69 3.7. Summary 71 Further Reading: A Simulation for Budgeting at the Portfolio Level 71 Exercises 74 Term Assignments 75 Part Two Estimation Process: What Must be Verified? 77 4. What Must be Verified in an Estimation Process: An Overview 79 4.1. Introduction 79 4.2. Verification of the Direct Inputs to An Estimation Process 81 4.2.1. Identification of the Estimation Inputs 81 4.2.2. Documenting the Quality of These Inputs 82 4.3. Verification of the Productivity Model 84 4.3.1. In-House Productivity Models 84 4.3.2. Externally Provided Models 85 4.4. Verification of the Adjustment Phase 86 4.5. Verification of the Budgeting Phase 87 4.6. Re-Estimation and Continuous Improvement to the Full Estimation Process 88 Further Reading: The Estimation Verification Report 89 Exercises 92 Term Assignments 93 5. Verification of the Dataset Used to Build the Models 94 5.1. Introduction 94 5.2. Verification of DIRECT Inputs 96 5.2.1. Verification of the Data Definitions and Data Quality 96 5.2.2. Importance of the Verification of the Measurement Scale Type 97 5.3. Graphical Analysis – One-Dimensional 100 5.4. Analysis of the Distribution of the Input Variables 102 5.4.1. Identification of a Normal (Gaussian) Distribution 102 5.4.2. Identification of Outliers: One-Dimensional Representation 103 5.4.3. Log Transformation 107 5.5. Graphical Analysis – Two-Dimensional 108 5.6. Size Inputs Derived from a Conversion Formula 111 5.7. Summary 112 Further Reading: Measurement and Quantification 113 Exercises 116 Term Assignments 117 Exercises–Further Reading Section 117 Term Assignments–Further Reading Section 118 6. Verification of Productivity Models 119 6.1. Introduction 119 6.2. Criteria Describing the Relationships Across Variables 120 6.2.1. Simple Criteria 120 6.2.2. Practical Interpretation of Criteria Values 122 6.2.3. More Advanced Criteria 124 6.3. Verification of the Assumptions of the Models 125 6.3.1. Three Key Conditions Often Required 125 6.3.2. Sample Size 126 6.4. Evaluation of Models by Their Own Builders 127 6.5. Models Already Built–Should You Trust Them? 128 6.5.1. Independent Evaluations: Small-Scale Replication Studies 128 6.5.2. Large-Scale Replication Studies 129 6.6. Lessons Learned: Distinct Models by Size Range 133 6.6.1. In Practice, Which is the Better Model? 138 6.7. Summary 138 Exercises 139 Term Assignments 139 7. Verification of the Adjustment Phase 141 7.1. Introduction 141 7.2. Adjustment Phase in the Estimation Process 142 7.2.1. Adjusting the Estimation Ranges 142 7.2.2. The Adjustment Phase in the Decision-Making Process: Identifying Scenarios for Managers 144 7.3. The Bundled Approach in Current Practices 145 7.3.1. Overall Approach 145 7.3.2. Detailed Approach for Combining the Impact of Multiple Cost Drivers in Current Models 146 7.3.3. Selecting and Categorizing Each Adjustment: The Transformation of Nominal Scale Cost Drivers intoNumbers 147 7.4. Cost Drivers as Estimation Submodels! 148 7.4.1. Cost Drivers as Step Functions 148 7.4.2. Step Function Estimation Submodels with Unknown Error Ranges 149 7.5. Uncertainty and Error Propagation 151 7.5.1. Error Propagation in Mathematical Formulas 151 7.5.2. The Relevance of Error Propagation in Models 153 Exercises 156 Term Assignments 157 Part Three Building Estimation Models: Data Collection and Analysis 159 8. Data Collection and Industry Standards: The ISBSG Repository 161 8.1. Introduction: Data Collection Requirements 161 8.2. The International Software Benchmarking Standards Group 163 8.2.1. The ISBSG Organization 163 8.2.2. The ISBSG Repository 164 8.3. ISBSG Data Collection Procedures 165 8.3.1. The Data Collection Questionnaire 165 8.3.2. ISBSG Data Definitions 167 8.4. Completed ISBSG Individual Project Benchmarking Reports: Some Examples 170 8.5. Preparing to Use the ISBSG Repository 173 8.5.1. ISBSG Data Extract 173 8.5.2. Data Preparation: Quality of the Data Collected 173 8.5.3. Missing Data: An Example with Effort Data 175 Further Reading 1: Benchmarking Types 177 Further Reading 2: Detailed Structure of the ISBSG Data Extract 179 Exercises 183 Term Assignments 183 9. Building and Evaluating Single Variable Models 185 9.1. Introduction 185 9.2. Modestly, One Variable at a Time 186 9.2.1. The Key Independent Variable: Software Size 186 9.2.2. Analysis of the Work–Effort Relationship in a Sample 188 9.3. Data Preparation 189 9.3.1. Descriptive Analysis 189 9.3.2. Identifying Relevant Samples and Outliers 189 9.4. Analysis of the Quality and Constraints of Models 193 9.4.1. Small Projects 195 9.4.2. Larger Projects 195 9.4.3. Implication for Practitioners 195 9.5. Other Models by Programming Language 196 9.6. Summary 202 Exercises 203 Term Assignments 203 10. Building Models with Categorical Variables 205 10.1. Introduction 205 10.2. The Available Dataset 206 10.3. Initial Model with a Single Independent Variable 208 10.3.1. Simple Linear Regression Model with Functional Size Only 208 10.3.2. Nonlinear Regression Models with Functional Size 208 10.4. Regression Models with Two Independent Variables 210 10.4.1. Multiple Regression Models with Two Independent Quantitative Variables 210 10.4.2. Multiple Regression Models with a Categorical Variable: Project Difficulty 210 10.4.3. The Interaction of Independent Variables 215 Exercises 216 Term Assignments 217 11. Contribution of Productivity Extremes in Estimation 218 11.1. Introduction 218 11.2. Identification of Productivity Extremes 219 11.3. Investigation of Productivity Extremes 220 11.3.1. Projects with Very Low Unit Effort 221 11.3.2. Projects with Very High Unit Effort 222 11.4. Lessons Learned for Estimation Purposes 224 Exercises 225 Term Assignments 225 12. Multiple Models from a Single Dataset 227 12.1. Introduction 227 12.2. Low and High Sensitivity to Functional Size Increases: Multiple Models 228 12.3. The Empirical Study 230 12.3.1. Context 230 12.3.2. Data Collection Procedures 231 12.3.3. Data Quality Controls 231 12.4. Descriptive Analysis 231 12.4.1. Project Characteristics 231 12.4.2. Documentation Quality and Its Impact on Functional Size Quality 233 12.4.3. Unit Effort (in Hours) 234 12.5. Productivity Analysis 234 12.5.1. Single Model with the Full Dataset 234 12.5.2. Model of the Least Productive Projects 235 12.5.3. Model of the Most Productive Projects 237 12.6. External Benchmarking with the ISBSG Repository 238 12.6.1. Project Selection Criteria and Samples 238 12.6.2. External Benchmarking Analysis 239 12.6.3. Further Considerations 240 12.7. Identification of the Adjustment Factors for Model Selection 241 12.7.1. Projects with the Highest Productivity (i.e., the Lowest Unit Effort) 241 12.7.2. Lessons Learned 242 Exercises 243 Term Assignments 243 13. Re-Estimation: A Recovery Effort Model 244 13.1. Introduction 244 13.2. The Need for Re-Estimation and Related Issues 245 13.3. The Recovery Effort Model 246 13.3.1. Key Concepts 246 13.3.2. Ramp-Up Process Losses 247 13.4. A Recovery Model When a Re-Estimation Need is Recognized at Time T > 0 248 13.4.1. Summary of Recovery Variables 248 13.4.2. A Mathematical Model of a Recovery Course in Re-Estimation 248 13.4.3. Probability of Underestimation −p(u) 249 13.4.4. Probability of Acknowledging the Underestimation on a Given Month −p(t) 250 Exercises 251 Term Assignments 251 References 253 Index 257

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