Search results for ""Author Samuel Kotz""
Wiley Statistical Size Distributions in Economics and Actuarial Sciences
Book SynopsisWith the growth of such fields as financial economics, so has the need for a thorough discussion of statistical size distributions.Trade Review"Researchers and teachers in econometrics and actuarial statistics will find this book an interesting source of information." (Journal of the American Statistical Association, December 2004) "The overall quality of the book is excellent. The material is well-written and well laid out…an indispensable, authoritative source of information…" (Zentralblatt MATH Database) “…an indispensable, authoritative source of information on a remarkably diverse variety of parametric models for applied statisticians and researchers…” (Zentralblatt Math, Vol.1044, No.19, 2004) "As a reference book, it is exceptionally well done, as are all of the projects undertaken by Prof. Kotz…The statistics profession has been greatly enriched by this particular effort." (Technometrics, November 2004) "…the authors have gone a long way towards achieving their ideal that 'a useful book on this subject matter should be interesting.” (Short Book Reviews, August 2004) “...traces the numerous statistical models of income distribution for Pareto’s model in the late 19th century to the latest ones.” (Quarterly of Applied Mathematics, Vol. LXII, No. 1, March 2004)Table of ContentsPreface. Acknowledgments. 1. Introduction. 1.1 Our Aims. 1.2 Types of Economic Size Distributions. 1.3 A Brief Historical Sketch of the Models for Studying Economic Size Distributions. 1.4 Stochastic Process Models for Size Distributions. 2. General Principles. 2.1 Some Concepts from Economics. 2.2 Hazard Rates, Mean Excess Functions, and Tailweight. 2.3 Systems of Distributions. 2.4 Generating Systems of Income Distributions. 3. Pareto Distributions. 3.1 Definitions. 3.2 History and Genesis. 3.3 Moments and Other Basic Properties. 3.4 Characterizations. 3.5 Lorenz Curve and Inequality Measures. 3.6 Estimation. 3.7 Empirical Results. 3.8 Stoppa Distributions. 3.9 Conic Distribution. 3.10 A "log-adjusted" Pareto Distribution. 3.11 Stable Distributions. 3.12 Further Pareto-type Distributions. 4. Lognormal Distribution. 4.1 Definition. 4.2 History and Genesis. 4.3 Moments and Other Basic Properties. 4.4 Characterizations. 4.5 Lorenz Curve and Inequality Measures. 4.6 Estimation. 4.7 Three-and four-parameter Lognormal Distributions. 4.8 Multivariate Lognormal Distribution. 4.9 Empirical Results. 4.10 Generalized Lognormal Distribution. 4.11 An Asymmetric Log-Laplace Distribution. 4.12 Related Distributions. 5. Gamma-type Size Distributions. 5.1 Generalized Gamma Distribution. 5.2 Gamma Distribution. 5.3 Log-gamma Distribution. 5.4 Inverse Gamma (Vinci) Distribution. 5.5 Weibull Distribution. 5.6 Log-Gompertz Distribution. 6. Beta-type Size Distributions. 6.1 (Generalized) Beta Distribution of the Second Kind. 6.2 Singh-Maddala Distribution. 6.3 Dagum Distribution. 6.4 Fisk (log-logistic) and Lomax Distributions. 6.5 (Generalized) Beta Distribution of the First Kind. 7. Miscellaneous Size Distributions. 7.1 Benini Distribution. 7.2 Davis Distribution. 7.3 Champernowne Distribution. 7.4 Benktander Distributions. Appendix A. Biographies. A.1 Vilfredo Federico Domaso Pareto, Marchese di Parigi. A.2 Rodolfo Benini. A.3 Max Otto Lorenz. A.4 Corrado Gini. A.5 Luigi Amoroso. A.6 Raffaele D’Addario. A.7 Robert Pierre Louis Gibrat. A.8 David Gawen Champernowne. Appendix B. Data on Size Distributions. Appendix C. Size Distributions. List of Symbols. References. Author Index. Subject Index.
£142.16
John Wiley & Sons Inc Leading Personalities in Statistical Sciences
Book SynopsisA fascinating chronicle of the lives and achievements of the men and women who helped shapethe science of statistics This handsomely illustrated volume will make enthralling reading for scientists, mathematicians, and science history buffs alike.Table of ContentsForerunners. Statistical Inference. Statistical Theory. Probability Theory. Government and Economic Statistics. Applications in Medicine and Agriculture. Applications in Science and Engineering. Appendix. Indexes. List of Series Titles.
£119.65
Wiley Univariate Discrete Distributions 3e Set
Book SynopsisThis Set Contains:Continuous Multivariate Distributions, Volume 1, Models and Applications, 2nd Edition by Samuel Kotz, N. Balakrishnan and Normal L. Johnson; Continuous Univariate Distributions, Volume 1, 2nd Edition by Samuel Kotz, N. Balakrishnan and Normal L. Johnson; Continuous Univariate Distributions, Volume 2, 2nd Edition by Samuel Kotz, N.Table of ContentsPreface xvii 1 Preliminary Information 1 2 Families of Discrete Distributions 74 3 Binomial Distribution 108 4 Poisson Distribution 156 5 Negative Binomial Distribution 208 6 Hypergeometric Distributions 251 7 Logarithmic and Lagrangian Distributions 302 8 Mixture Distributions 343 9 Stopped-Sum Distributions 381 10 Matching, Occupancy, Runs, and q-Series Distributions 430 11 Parametric Regression Models and Miscellanea 478 Bibliography 535 Abbreviations 631 Index 633
£877.46
Cambridge University Press Multivariate TDistributions and Their Applications
a huge range and FREE tracked UK delivery on ALL orders.
£104.50
John Wiley & Sons Inc Univariate Discrete Distributions
Book SynopsisThis Set Contains: Continuous Multivariate Distributions, Volume 1, Models and Applications, 2nd Edition by Samuel Kotz, N. Balakrishnan and Normal L. Johnson Continuous Univariate Distributions, Volume 1, 2nd Edition by Samuel Kotz, N. Balakrishnan and Normal L. Johnson Continuous Univariate Distributions, Volume 2, 2nd Edition by Samuel Kotz, N. Balakrishnan and Normal L. Johnson Discrete Multivariate Distributions by Samuel Kotz, N. Balakrishnan and Normal L. Johnson Univariate Discrete Distributions, 3rd Edition by Samuel Kotz, N. Balakrishnan and Normal L. Johnson Discover the latest advances in discrete distributions theory The Third Edition of the critically acclaimed Univariate Discrete Distributions provides a self-contained, systematic treatment of the theory, derivation, and application of probability distributions for count data. Generalized zeta-function and q-series distributTrade Review“With its thorough coverage and balanced presentation of theory and application, this is an excellent and essential reference for statisticians and mathematicians.” (Xolosepo, 27 October 2012) "The authors continue to do a praise-worthy job of making the material accessible in the third edition. This book should be on every library's shelf." (Journal of the American Statistical Association, September 2006) "These authors have achieved considerable renown for their comprehensive books on statistical distributions." (Technometrics, August 2006) "Encyclopedic in nature, the book continues to be a valuable reference." (Mathematical Reviews, 2006d) "This is an important book that should be part of every statistician's library." (MAA Reviews, January 2, 2006)Table of ContentsPreface xvii 1 Preliminary Information 1 1.1 Mathematical Preliminaries 1 1.1.1 Factorial and Combinatorial Conventions 1 1.1.2 Gamma and Beta Functions 5 1.1.3 Finite Difference Calculus 10 1.1.4 Differential Calculus 14 1.1.5 Incomplete Gamma and Beta Functions and Other Gamma-Related Functions 16 1.1.6 Gaussian Hypergeometric Functions 20 1.1.7 Confluent Hypergeometric Functions (Kummer’s Functions) 23 1.1.8 Generalized Hypergeometric Functions 26 1.1.9 Bernoulli and Euler Numbers and Polynomials 29 1.1.10 Integral Transforms 32 1.1.11 Orthogonal Polynomials 32 1.1.12 Basic Hypergeometric Series 34 1.2 Probability and Statistical Preliminaries 37 1.2.1 Calculus of Probabilities 37 1.2.2 Bayes’s Theorem 41 1.2.3 Random Variables 43 1.2.4 Survival Concepts 45 1.2.5 Expected Values 47 1.2.6 Inequalities 49 1.2.7 Moments and Moment Generating Functions 50 1.2.8 Cumulants and Cumulant Generating Functions 54 1.2.9 Joint Moments and Cumulants 56 1.2.10 Characteristic Functions 57 1.2.11 Probability Generating Functions 58 1.2.12 Order Statistics 61 1.2.13 Truncation and Censoring 62 1.2.14 Mixture Distributions 64 1.2.15 Variance of a Function 65 1.2.16 Estimation 66 1.2.17 General Comments on the Computer Generation of Discrete Random Variables 71 1.2.18 Computer Software 73 2 Families of Discrete Distributions 74 2.1 Lattice Distributions 74 2.2 Power Series Distributions 75 2.2.1 Generalized Power Series Distributions 75 2.2.2 Modified Power Series Distributions 79 2.3 Difference-Equation Systems 82 2.3.1 Katz and Extended Katz Families 82 2.3.2 Sundt and Jewell Family 85 2.3.3 Ord’s Family 87 2.4 Kemp Families 89 2.4.1 Generalized Hypergeometric Probability Distributions 89 2.4.2 Generalized Hypergeometric Factorial Moment Distributions 96 2.5 Distributions Based on Lagrangian Expansions 99 2.6 Gould and Abel Distributions 101 2.7 Factorial Series Distributions 103 2.8 Distributions of Order-k 105 2.9 q-Series Distributions 106 3 Binomial Distribution 108 3.1 Definition 108 3.2 Historical Remarks and Genesis 109 3.3 Moments 109 3.4 Properties 112 3.5 Order Statistics 116 3.6 Approximations, Bounds, and Transformations 116 3.6.1 Approximations 116 3.6.2 Bounds 122 3.6.3 Transformations 123 3.7 Computation, Tables, and Computer Generation 124 3.7.1 Computation and Tables 124 3.7.2 Computer Generation 125 3.8 Estimation 126 3.8.1 Model Selection 126 3.8.2 Point Estimation 126 3.8.3 Confidence Intervals 130 3.8.4 Model Verification 133 3.9 Characterizations 134 3.10 Applications 135 3.11 Truncated Binomial Distributions 137 3.12 Other Related Distributions 140 3.12.1 Limiting Forms 140 3.12.2 Sums and Differences of Binomial-Type Variables 140 3.12.3 Poissonian Binomial, Lexian, and Coolidge Schemes 144 3.12.4 Weighted Binomial Distributions 149 3.12.5 Chain Binomial Models 151 3.12.6 Correlated Binomial Variables 151 4 Poisson Distribution 156 4.1 Definition 156 4.2 Historical Remarks and Genesis 156 4.2.1 Genesis 156 4.2.2 Poissonian Approximations 160 4.3 Moments 161 4.4 Properties 163 4.5 Approximations, Bounds, and Transformations 167 4.6 Computation, Tables, and Computer Generation 170 4.6.1 Computation and Tables 170 4.6.2 Computer Generation 171 4.7 Estimation 173 4.7.1 Model Selection 173 4.7.2 Point Estimation 174 4.7.3 Confidence Intervals 176 4.7.4 Model Verification 178 4.8 Characterizations 179 4.9 Applications 186 4.10 Truncated and Misrecorded Poisson Distributions 188 4.10.1 Left Truncation 188 4.10.2 Right Truncation and Double Truncation 191 4.10.3 Misrecorded Poisson Distributions 193 4.11 Poisson–Stopped Sum Distributions 195 4.12 Other Related Distributions 196 4.12.1 Normal Distribution 196 4.12.2 Gamma Distribution 196 4.12.3 Sums and Differences of Poisson Variates 197 4.12.4 Hyper-Poisson Distributions 199 4.12.5 Grouped Poisson Distributions 202 4.12.6 Heine and Euler Distributions 205 4.12.7 Intervened Poisson Distributions 205 5 Negative Binomial Distribution 208 5.1 Definition 208 5.2 Geometric Distribution 210 5.3 Historical Remarks and Genesis of Negative Binomial Distribution 212 5.4 Moments 215 5.5 Properties 217 5.6 Approximations and Transformations 218 5.7 Computation and Tables 220 5.8 Estimation 222 5.8.1 Model Selection 222 5.8.2 P Unknown 222 5.8.3 Both Parameters Unknown 223 5.8.4 Data Sets with a Common Parameter 226 5.8.5 Recent Developments 227 5.9 Characterizations 228 5.9.1 Geometric Distribution 228 5.9.2 Negative Binomial Distribution 231 5.10 Applications 232 5.11 Truncated Negative Binomial Distributions 233 5.12 Related Distributions 236 5.12.1 Limiting Forms 236 5.12.2 Extended Negative Binomial Model 237 5.12.3 Lagrangian Generalized Negative Binomial Distribution 239 5.12.4 Weighted Negative Binomial Distributions 240 5.12.5 Convolutions Involving Negative Binomial Variates 241 5.12.6 Pascal–Poisson Distribution 243 5.12.7 Minimum (Riff–Shuffle) and Maximum Negative Binomial Distributions 244 5.12.8 Condensed Negative Binomial Distributions 246 5.12.9 Other Related Distributions 247 6 Hypergeometric Distributions 251 6.1 Definition 251 6.2 Historical Remarks and Genesis 252 6.2.1 Classical Hypergeometric Distribution 252 6.2.2 Beta–Binomial Distribution, Negative (Inverse) Hypergeometric Distribution: Hypergeometric Waiting-Time Distribution 253 6.2.3 Beta–Negative Binomial Distribution: Beta–Pascal Distribution, Generalized Waring Distribution 256 6.2.4 Pólya Distributions 258 6.2.5 Hypergeometric Distributions in General 259 6.3 Moments 262 6.4 Properties 265 6.5 Approximations and Bounds 268 6.6 Tables Computation and Computer Generation 271 6.7 Estimation 272 6.7.1 Classical Hypergeometric Distribution 273 6.7.2 Negative (Inverse) Hypergeometric Distribution: Beta–Binomial Distribution 274 6.7.3 Beta–Pascal Distribution 276 6.8 Characterizations 277 6.9 Applications 279 6.9.1 Classical Hypergeometric Distribution 279 6.9.2 Negative (Inverse) Hypergeometric Distribution: Beta–Binomial Distribution 281 6.9.3 Beta–Negative Binomial Distribution: Beta–Pascal Distribution, Generalized Waring Distribution 283 6.10 Special Cases 283 6.10.1 Discrete Rectangular Distribution 283 6.10.2 Distribution of Leads in Coin Tossing 286 6.10.3 Yule Distribution 287 6.10.4 Waring Distribution 289 6.10.5 Narayana Distribution 291 6.11 Related Distributions 293 6.11.1 Extended Hypergeometric Distributions 293 6.11.2 Generalized Hypergeometric Probability Distributions 296 6.11.3 Generalized Hypergeometric Factorial Moment Distributions 298 6.11.4 Other Related Distributions 299 7 Logarithmic and Lagrangian Distributions 302 7.1 Logarithmic Distribution 302 7.1.1 Definition 302 7.1.2 Historical Remarks and Genesis 303 7.1.3 Moments 305 7.1.4 Properties 307 7.1.5 Approximations and Bounds 309 7.1.6 Computation, Tables, and Computer Generation 310 7.1.7 Estimation 311 7.1.8 Characterizations 315 7.1.9 Applications 316 7.1.10 Truncated and Modified Logarithmic Distributions 317 7.1.11 Generalizations of the Logarithmic Distribution 319 7.1.12 Other Related Distributions 321 7.2 Lagrangian Distributions 325 7.2.1 Otter’s Multiplicative Process 326 7.2.2 Borel Distribution 328 7.2.3 Consul Distribution 329 7.2.4 Geeta Distribution 330 7.2.5 General Lagrangian Distributions of the First Kind 331 7.2.6 Lagrangian Poisson Distribution 336 7.2.7 Lagrangian Negative Binomial Distribution 340 7.2.8 Lagrangian Logarithmic Distribution 341 7.2.9 Lagrangian Distributions of the Second Kind 342 8 Mixture Distributions 343 8.1 Basic Ideas 343 8.1.1 Introduction 343 8.1.2 Finite Mixtures 344 8.1.3 Varying Parameters 345 8.1.4 Bayesian Interpretation 347 8.2 Finite Mixtures of Discrete Distributions 347 8.2.1 Parameters of Finite Mixtures 347 8.2.2 Parameter Estimation 349 8.2.3 Zero-Modified and Hurdle Distributions 351 8.2.4 Examples of Zero-Modified Distributions 353 8.2.5 Finite Poisson Mixtures 357 8.2.6 Finite Binomial Mixtures 358 8.2.7 Other Finite Mixtures of Discrete Distributions 359 8.3 Continuous and Countable Mixtures of Discrete Distributions 360 8.3.1 Properties of General Mixed Distributions 360 8.3.2 Properties of Mixed Poisson Distributions 362 8.3.3 Examples of Poisson Mixtures 365 8.3.4 Mixtures of Binomial Distributions 373 8.3.5 Examples of Binomial Mixtures 374 8.3.6 Other Continuous and Countable Mixtures of Discrete Distributions 376 8.4 Gamma and Beta Mixing Distributions 378 9 Stopped-Sum Distributions 381 9.1 Generalized and Generalizing Distributions 381 9.2 Damage Processes 386 9.3 Poisson–Stopped Sum (Multiple Poisson) Distributions 388 9.4 Hermite Distribution 394 9.5 Poisson–Binomial Distribution 400 9.6 Neyman Type A Distribution 403 9.6.1 Definition 403 9.6.2 Moment Properties 405 9.6.3 Tables and Approximations 406 9.6.4 Estimation 407 9.6.5 Applications 409 9.7 Pólya–Aeppli Distribution 410 9.8 Generalized Pólya–Aeppli (Poisson–Negative Binomial) Distribution 414 9.9 Generalizations of Neyman Type A Distribution 416 9.10 Thomas Distribution 421 9.11 Borel–Tanner Distribution: Lagrangian Poisson Distribution 423 9.12 Other Poisson–Stopped Sum (multiple Poisson) Distributions 425 9.13 Other Families of Stopped-Sum Distributions 426 10 Matching, Occupancy, Runs, and q-Series Distributions 430 10.1 Introduction 430 10.2 Probabilities of Combined Events 431 10.3 Matching Distributions 434 10.4 Occupancy Distributions 439 10.4.1 Classical Occupancy and Coupon Collecting 439 10.4.2 Maxwell–Boltzmann, Bose–Einstein, and Fermi–Dirac Statistics 444 10.4.3 Specified Occupancy and Grassia–Binomial Distributions 446 10.5 Record Value Distributions 448 10.6 Runs Distributions 450 10.6.1 Runs of Like Elements 450 10.6.2 Runs Up and Down 453 10.7 Distributions of Order k 454 10.7.1 Early Work on Success Runs Distributions 454 10.7.2 Geometric Distribution of Order k 456 10.7.3 Negative Binomial Distributions of Order k 458 10.7.4 Poisson and Logarithmic Distributions of Order k 459 10.7.5 Binomial Distributions of Order k 461 10.7.6 Further Distributions of Order k 463 10.8 q-Series Distributions 464 10.8.1 Terminating Distributions 465 10.8.2 q-Series Distributions with Infinite Support 470 10.8.3 Bilateral q-Series Distributions 474 10.8.4 q-Series Related Distributions 476 11 Parametric Regression Models and Miscellanea 478 11.1 Parametric Regression Models 478 11.1.1 Introduction 478 11.1.2 Tweedie–Poisson Family 480 11.1.3 Negative Binomial Regression Models 482 11.1.4 Poisson Lognormal Model 483 11.1.5 Poisson–Inverse Gaussian (Sichel) Model 484 11.1.6 Poisson Polynomial Distribution 487 11.1.7 Weighted Poisson Distributions 488 11.1.8 Double-Poisson and Double-Binomial Distributions 489 11.1.9 Simplex–Binomial Mixture Model 490 11.2 Miscellaneous Discrete Distributions 491 11.2.1 Dandekar’s Modified Binomial and Poisson Models 491 11.2.2 Digamma and Trigamma Distributions 492 11.2.3 Discrete Adès Distribution 494 11.2.4 Discrete Bessel Distribution 495 11.2.5 Discrete Mittag–Leffler Distribution 496 11.2.6 Discrete Student’s t Distribution 498 11.2.7 Feller–Arley and Gegenbauer Distributions 499 11.2.8 Gram–Charlier Type B Distributions 501 11.2.9 “Interrupted” Distributions 502 11.2.10 Lost-Games Distributions 503 11.2.11 Luria–Delbrück Distribution 505 11.2.12 Naor’s Distribution 507 11.2.13 Partial-Sums Distributions 508 11.2.14 Queueing Theory Distributions 512 11.2.15 Reliability and Survival Distributions 514 11.2.16 Skellam–Haldane Gene Frequency Distribution 519 11.2.17 Steyn’s Two-Parameter Power Series Distributions 521 11.2.18 Univariate Multinomial-Type Distributions 522 11.2.19 Urn Models with Stochastic Replacements 524 11.2.20 Zipf-Related Distributions 526 11.2.21 Haight’s Zeta Distributions 533 Bibliography 535 Abbreviations 631 Index 633
£206.96
Wiley Continuous Univariate Distributions Volume 2
Book SynopsisThis volume presents a detailed description of the statistical distributions that are commonly applied to such fields as engineering, business, economics and the behavioural, biological and environmental sciences.Table of ContentsExtreme Value Distributions. Logistic Distribution. Laplace (Double Exponential) Distributions. Beta Distributions. Uniform (Rectangular) Distributions. F-Distributions. t-Distributions. Noncentral x^2 Distributions. Noncentral F-Distributions. Noncentral t-Distributions. Distributions of Correlation Coefficients. Lifetime Distributions and Miscellaneous Orderings. Abbreviations. Indexes.
£206.96
John Wiley & Sons Inc Continuous Univariate Distributions Volume 1
Book SynopsisThe definitive reference for statistical distributions Continuous Univariate Distributions, Volume 1 offers comprehensive guidance toward the most commonly used statistical distributions, including normal, lognormal, inverse Gaussian, Pareto, Cauchy, gamma distributions and more. Each distribution includes clear definitions and properties, plus methods of inference, applications, algorithms, characterizations, and reference to other related distributions. Organized for easy navigation and quick reference, this book is an invaluable resource for investors, data analysts, or anyone working with statistical distributions on a regular basis.Table of ContentsContinuous Distributions (General). Normal Distributions. Lognormal Distributions. Inverse Gaussian (Wald) Distributions. Cauchy Distribution. Gamma Distributions. Chi-Square Distributions Including Chi and Rayleigh. Exponential Distributions. Pareto Distributions. Weibull Distributions. Abbreviations. Indexes.
£206.96
John Wiley & Sons Inc Discrete Multivariate Distributions
Book SynopsisThis book is an expanded and updated version of Chapter 11 of Distributions in Statistics: Discrete Distributions, written by Norman L. Johnson and Samuel Kotz in 1969. It provides detailed explanations of discrete multivariate distributions, including their properties and usefulness.Table of ContentsGeneral Remarks. Multinominal Distributions. Negative Multinominal and Other Multinominal-RelatedDistributions. Multivariate Poisson Distributions. Multivariate Power Series Distributions. Multivariate Hypergeometric and Related Distributions. Multivariate Polya-Eggenberger Distributions. Multivariate Ewens Distribution. Multivariate Distributions of Order s. Miscellaneous Distributions. Abbreviations. Indexes.
£216.86
John Wiley & Sons Inc Encyclopedia of Statistical Sciences Volume 12
Book Synopsis
£389.45
John Wiley & Sons Inc Continuous Multivariate Distributions 2e V 1
Book SynopsisThis book concentrates on a variety of multivariate distributional models (other than the normal and related sampling distributions). It covers a wide range of models from multivariate (MV) exponential, MV extremevalue and MV gamma, to MV beta (or dirichlet) and MV pareto, to name but a few.Trade ReviewThis book brings one right up to date and is a worthy addition to the existing set of second editions of the other volumes of Distributions in Statistics. It will remain the key reference for many years. (Short Book Reviews, Vol. 20, No. 3, December 2000) [...] Continuous Multivariate Distributions is a unique and valuable source of information on multivariate distributions. This book, and the rest of this venerable and important series, should be on the shelves of every statistician. (JASA June 2001) For certain it will serve as the primary source for continuous multivariate statistical distributions for a long time. (Zentralblatt Math, Volume 946, No 21, 2000) "...provides a remarkably comprehensive, self-contained resource for this important statistical area." (Mathematical Reviews, Issue 2001h) "It will remain the key reference for many years." (Short Book Reviews, December 2000) "...will serve as the primary source for continuous multivariate statistical distributions for a long time." (Zentralblatt MATH, Vol. 946, No. 21) "Like its predecessors, this monograph is a most welcome addition to the statistical literature. We are looking forward to Volume 2..." (Statistical Papers, Vol. 42, No. 3, 2001)Table of ContentsSystems of Continuous Multivariate Distributions. Multivariate Normal Distributions. Bivariate and Trivariate Normal Distributions. Multivariate Exponential Distributions. Multivariate Gamma Distributions. Dirichlet and Inverted Dirichlet Distributions. Multivariate Liouville Distributions. Multivariate Logistic Distributions. Multivariate Pareto Distributions. Bivariate and Multivariate Extreme Value Distributions. Natural Exponential Families. Indexes.
£206.96
John Wiley & Sons Inc Encyclopedia of Statistical Sciences Volume 15
Book SynopsisENCYCLOPEDIA OF STATISTICAL SCIENCES
£368.96
World Scientific Publishing Co Pte Ltd Arthur L Bowley: A Pioneer In Modern Statistics
Book SynopsisArthur Lyon Bowley, the founding father of modern statistics, was an important and colorful figure and a leader in cementing the foundations of statistical methodology, including survey methodology, and of the applications of statistics to economical and social issues during the late 19th and early 20th centuries. In many respects, he was ahead of his time.The giants in this field around that time were largely concentrated in the British Isles and Scandinavian countries; among these contributors, Arthur Bowley was one of the most active in revolutionizing statistical methodology and its economic applications. However, Bowley has been vastly undervalued by subsequent commentators — while hundreds of articles and books have been written on Karl Pearson, those on Arthur Bowley amount to a dozen or less. This book seeks to remedy this and fill in an important omission in the monographical literature on the history of statistics. In particular, the recent resurgence of interest in poverty research has led to a renewed interest in Bowley's legacy.Table of ContentsBiography and Scientific Activities of A L Bowley; Statistical Papers and Books; Economic Papers and Books; A L Bowley's Everlasting Effect.
£153.00
Imperial College Press Correlation And Dependence
Book SynopsisThe concept of dependence permeates the Earth and its inhabitants in a most profound manner. Examples of interdependent meteorological phenomena in nature and interdependence in the medical, social, and political aspects of our existence, not to mention the economic structures, are too numerous to be cited individually. Moreover, the dependence is obviously not deterministic but of a stochastic nature. However, it seems that none of the departments of statistics, engineering, economics and mathematics in the academic institutions throughout the world offer courses dealing with dependence concepts and measures.This book can thus be viewed as an attempt to remedy the situation, and it has been written for a graduate course or a seminar on correlation and dependence concepts and measures. A modest background in mathematical statistics and probability and integral calculus is required. The book is not a full-scale expedition up another statistical Alp. Rather, it is a tour over a somewhat neglected but important terrain. The chapter on correlation is written for a layman.Trade Review"... it should be on the shelf of any statistician who must deal with dependence." Biometrics, Dec 2001 "The monograph should be useful to any person who does research in the area of dependence, and to any person who may wish to correctly apply positive dependence notions and orderings in practice." Mathematical Reviews, 2002Table of ContentsNotations and definitions; correlation and dependence - an introspection; concepts of dependence and stochastic ordering; copulas; Farlie-Gumbel-Morgenstern models of dependence; global versus local dependence between random variables.
£73.15
Imperial College Press Extreme Value Distributions
Book SynopsisThis important book provides an up-to-date comprehensive and down-to-earth survey of the theory and practice of extreme value distributions — one of the most prominent success stories of modern applied probability and statistics. Originated by E J Gumbel in the early forties as a tool for predicting floods, extreme value distributions evolved during the last 50 years into a coherent theory with applications in practically all fields of human endeavor where maximal or minimal values (the so-called extremes) are of relevance. The book is of usefulness both for a beginner with a limited probabilistic background and to expert in the field.Trade Review"... this is a solid mathematical treatment of some topics of extreme value theory addressed to professionals in the field of statistical distributions and statistical inference as well as to people interested in the applications of extreme value distributions. The book can be used very constructively as a textbook or a reference book." Mathematics Abstracts "Extreme Value Distributions is a well-organized, workmanlike treatment of the subject ... Researchers who need to deal with extreme value problems but are novice statisticians will greatly appreciate the clarity of presentation in this book and will wish to obtain copy." The Statisticians, Royal Society of Statistics "The wealth of material and the critical comments by the authors on the various problems considered in the monograph should make it a valuable resource even for a specialist." Mathematical ReviewsTable of ContentsUnivariate extreme value distributions; generalized extreme value distributions; multivariate extreme value distributions.
£65.55
World Scientific Publishing Co Pte Ltd Beyond Beta: Other Continuous Families Of Distributions With Bounded Support And Applications
Book SynopsisStatistical distributions are fundamental to Statistical Science and are a prime indispensable tool for its applications. This monograph is the first to examine an important but somewhat neglected field — univariate continuous distribution on a bounded domain, excluding the beta distribution. It provides an elementary but thorough discussion of “novel” contributions developed in recent years, such as the two-sided power, generalized trapezoidal and generalized Topp and Leone distributions, among others. It discusses a general framework for constructing two-sided distributions and some of its properties. It contains a comprehensive chapter on the triangular distribution as well as a chapter on earlier extensions not emphasized in existing literature. Special attention is given to estimation, in particular, non-standard maximum likelihood procedures. The applications are drawn mainly from the econometric and engineering domains.Table of ContentsThe Triangular Distribution; Some Early Extensions of the Triangular Distribution; The Standard Two-Sided Power Distribution; The Two-Sided Power Distribution; The Generalized Trapezoidal Distribution; Uneven Two-Sided Power Distributions; The Reflected Generalized Topp and Leone Distribution; A Generalized Framework for Two-Sided Distributions.
£90.25
