Mathematical and statistical software Books

Book SynopsisEach statistical chapter in the second part relies on one or more real biomedical data sets, kindly made available by the Bordeaux School of Public Health (Institut de Santé Publique, d'Épidémiologie et de Développement - ISPED) and described at the beginning of the book.Trade ReviewFrom the book reviews:“This is a great addition to the chorus of books on R. It is a clear an excellent resource for teaching courses on data analysis and statistical computing using R at the graduate and advanced undergraduate levels. The book can be an asset for data scientists, and even more broadly for a wide variety of users including students, teachers, researchers, software engineers, and others whose work involves statistics, mathematics, and computer science.” (Yousri El Fattah, Computing Reviews, January, 2015)Table of ContentsForeward.- Basic Concepts and Data Organisation.- Importing, Exporting and Producing Data.- Data Manipulation, Functions.- R and its Documentation.- Drawing Curves and Plots.- Programming in R.- Managing Sessions.- Basic Mathematics.- Descriptive Statistics.- A Better Understanding of Random Variables.- Confidence Intervals and Hypothesis Testing.- Simple and Multiple Linear Regression.- Elementary Analysis of Variance.- Installing R and R Packages.- References.- Indices.- Solutions.

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Book SynopsisThis handy reference book detailing the intricacies of R covers version 4.x features, including numerous and significant changes to syntax, strings, reference counting, grid units, and more. Starting with the basic structure of R, the book takes you on a journey through the terminology used in R and the syntax required to make R work. You will find looking up the correct form for an expression quick and easy. Some of the new material includes information on RStudio, S4 syntax, working with character strings, and an example using the Twitter API. With a copy of the R 4 Quick Syntax Reference in hand, you will find that you are able to use the multitude of functions available in R and are even able to write your own functions to explore and analyze data. What You Will LearnDiscover the modes and classes of R objects and how to use themUse both packaged and user-created functions in RImport/export data and create new data objects in RCreate descriptive functions and manipulate objecTable of ContentsPart 1: R Basics1. Downloading R and Setting Up a File System2. The R Prompt3. Assignments and OperatorsPart 2: Kinds of Objects4. Modes of Objects5. Classes of ObjectsPart 3: Functions6. Packaged Functions7. User Created Functions8. How to Use a FunctionPart 4: I/O and Manipulating Objects9. Importing/Creating Data10. Exporting from R11. Descriptive Functions and Manipulating ObjectsPart 5: Flow control12. Flow Control13. Examples of Flow Control14. The Functions ifelse() and switch()Part 6: Some Common Functions, Packages and Techniques15. Some Common Functions16. The Packages base, stats and graphics17. The Tricks of the Trade

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Book SynopsisAnyone who wants to analyze data will find something useful here-even if you don't have a background in mathematics, statistics, or computer programming. If you want to examine data related to your work, this book is the ideal way to start.

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Book SynopsisApplied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning.Trade Review“…In teaching a data science course…I use a range of different resources because I need to cover working with data, model evaluation, and machine learning methods. The next time I teach this course, I will use only this book because it covers all of these aspects of the field.” (Louis Luangkesorn, lugerpitt.blogspot.com, June 2015) “There are a wide variety of books available on predictive analytics and data modeling around the web…we’ve carefully selected the following 10 books, based on relevance, popularity, online ratings, and their ability to add value to your business. 1. Applied Predictive Modeling.” (Timothy King, Business Intelligence Solutions Review, solutions-review.com, June 2015) "Applied Predictive Modeling aims to expose many of these techniques in a very readable and self-contained book. This is a very applied and hands-on book. It guides the reader through many examples that serve to illustrate main points, and it raises possible issues and considerations that are oftentimes overlooked or not sufficiently reflected upon. Highly recommended." (Bojan Tunguz, tunguzreview.com, June 2015)“This monograph presents a very friendly practical course on prediction techniques for regression and classification models… It is a well-written book very useful to students and practitioners who need an immediate and helpful way to apply complex statistical techniques.” (Stan Lipovetsky, Technometrics, Vol. 56 (3), August 2014)“In my judgment, Applied Predictive Modeling by Max Kuhn and Kjell Johnson (Springer 2013) ought to be at the very top of the reading list …They come across like coaches who really, really want you to be able to do this…Applied Predictive Modeling is a remarkable text…it is the succinct distillation of years of experience of two expert modelers…” (Joseph Rickert, blog.revolutionanalytics.com, June 2014)Table of ContentsGeneral Strategies.- Regression Models.- Classification Models.- Other Considerations.- Appendix.- References.- Indices.

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Book SynopsisThis third edition of MATLAB Guide completely revises and updates the best-selling second edition and is more than 25 per cent longer. The book remains a lively, concise introduction to the most popular and important features of MATLAB and the Symbolic Math Toolbox.

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Book Synopsis“If mathematical modeling is the process of turning real phenomena into mathematical abstractions, then numerical computation is largely about the transformation from abstract mathematics to concrete reality. Many science and engineering disciplines have long benefited from the tremendous value of the correspondence between quantitative information and mathematical manipulation.” -from the PrefaceFundamentals of Numerical Computation is an advanced undergraduate-level introduction to the mathematics and use of algorithms for the fundamental problems of numerical computation: linear algebra, finding roots, approximating data and functions, and solving differential equations. The book is organized with simpler methods in the first half and more advanced methods in the second half, allowing use for either a single course or a sequence of two courses. The authors take readers from basic to advanced methods, illustrating them with over 200 self-contained MATLAB functions and examples designed for those with no prior MATLAB experience. Although the text provides many examples, exercises, and illustrations, the aim of the authors is not to provide a cookbook per se, but rather an exploration of the principles of cooking.Professors Driscoll and Braun have developed an online resource that includes well-tested materials related to every chapter. Among these materials are lecture-related slides and videos, ideas for student projects, laboratory exercises, computational examples and scripts, and all the functions presented in the book.

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Book SynopsisThis book presents an introduction to structural equation modeling (SEM) and facilitates the access of students and researchers in various scientific fields to this powerful statistical tool. It offers a didactic initiation to SEM as well as to the open-source software, lavaan, and the rich and comprehensive technical features it offers. Structural Equation Modeling with lavaan thus helps the reader to gain autonomy in the use of SEM to test path models and dyadic models, perform confirmatory factor analyses and estimate more complex models such as general structural models with latent variables and latent growth models. SEM is approached both from the point of view of its process (i.e. the different stages of its use) and from the point of view of its product (i.e. the results it generates and their reading). Table of ContentsPreface ix Introduction xi Chapter 1 Structural Equation Modeling 1 1.1 Basic concepts 2 1.1.1 Covariance and bivariate correlation 2 1.1.2 Partial correlation 5 1.1.3 Linear regression analysis 7 1.1.4 Standard error of the estimate 10 1.1.5 Factor analysis 11 1.1.6 Data distribution normality 18 1.2 Basic principles of SEM 21 1.2.1 Estimation methods (estimators) 27 1.3 Model evaluation of the solution of the estimated model 36 1.3.1 Overall goodness-of-fit indices 36 1.3.2 Local fit indices (parameter estimates) 43 1.3.3 Modification indices 44 1.4 Confirmatory approach in SEM 45 1.5 Basic conventions of SEM 47 1.6 Place and status of variables in a hypothetical model 49 1.7 Conclusion 49 1.8 Further reading 50 Chapter 2 Structural Equation Modeling Software 53 2.1 R environment 54 2.1.1 Installing R software 55 2.1.2 R console 55 2.2 lavaan 58 2.2.1 Installing the lavaan package 58 2.2.2 Launching lavaan 58 2.3 Preparing and importing a dataset 60 2.3.1 Entry and import of raw data 60 2.3.2 What to do in the absence of raw data? 63 2.4 Major operators of lavaan syntax 65 2.5 Main steps in using lavaan 66 2.6 lavaan fitting functions 68 Chapter 3 Steps in Structural Equation Modeling 69 3.1 The theoretical model and its conceptual specification 70 3.2 Model parameters and model identification 71 3.3 Models with observed variables (path models) 73 3.3.1 Identification of a path model 74 3.3.2 Model specification using lavaan (step 2) 76 3.3.3 Direct and indirect effects 78 3.3.4 The statistical significance of indirect effects 80 3.3.5 Model estimation with lavaan (step 3) 81 3.3.6 Model evaluation (step 4) 82 3.3.7 Recursive and non-recursive models 83 3.3.8 Illustration of a path analysis model 85 3.4 Actor-partner interdependence model 90 3.4.1 Specifying and estimating an APIM with lavaan 92 3.4.2 Evaluation of the solution 93 3.4.3 Evaluating the APIM re-specified with equality constraints 94 3.5 Models with latent variables (measurement models and structural models) 95 3.5.1 The measurement model or Confirmatory Factor Analysis 97 3.6 Hybrid models 148 3.7 Measure with a single-item indicator 149 3.8 General structural model including single-item latent variables with a single indicator 151 3.9 Conclusion 152 3.10 Further reading 155 Chapter 4 Advanced Topics: Principles and Applications 157 4.1 Multigroup analysis 157 4.1.1 The steps of MG-CFA 162 4.1.2 Model solutions and model comparison tests 166 4.1.3 Total invariance versus partial invariance 171 4.1.4 Specification of a partial invariance in lavaan syntax 172 4.2 Latent trait-state models 172 4.2.1 The STARTS model 173 4.2.2 The Trait-State-Occasion Model 197 4.2.3 Concluding remarks 211 4.3 Latent growth models 213 4.3.1 General overview 213 4.3.2 Illustration of an univariate linear growth model 223 4.3.3 Illustration of an univariate non-linear (quadratic) latent growth model 228 4.3.4 Conditional latent growth model 232 4.3.5 Second-order latent growth model 240 4.4 Further reading 249 References 251 Index 269

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Book SynopsisData science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are produced today, and to improve prediction. Special kinds of data (symbolic, network, complex, compositional) are increasingly frequent in data science. These data require specific methodologies, but there is a lack of reference work in this field. Advances in Data Science fills this gap. It presents a collection of up-to-date contributions by eminent scholars following two international workshops held in Beijing and Paris. The 10 chapters are organized into four parts: Symbolic Data, Complex Data, Network Data and Clustering. They include fundamental contributions, as well as applications to several domains, including business and the social sciences. Table of ContentsPreface xi Part 1. Symbolic Data 1 Chapter 1. Explanatory Tools for Machine Learning in the Symbolic Data Analysis Framework 3Edwin DIDAY 1.1. Introduction 4 1.2. Introduction to Symbolic Data Analysis 6 1.2.1. What are complex data? 6 1.2.2. What are “classes” and “class of complex data”? 7 1.2.3. Which kind of class variability? 7 1.2.4. What are “symbolic variables” and “symbolic data tables”? 7 1.2.5. Symbolic Data Analysis (SDA) 9 1.3. Symbolic data tables from Dynamic Clustering Method and EM 10 1.3.1. The “dynamical clustering method” (DCM) 10 1.3.2. Examples of DCM applications 10 1.3.3. Clustering methods by mixture decomposition 12 1.3.4. Symbolic data tables from clustering 13 1.3.5. A general way to compare results of clustering methods by the “explanatory power” of their associated symbolic data table 15 1.3.6. Quality criteria of classes and variables based on the cells of the symbolic data table containing intervals or inferred distributions 15 1.4. Criteria for ranking individuals, classes and their bar chart descriptive symbolic variables 16 1.4.1. A theoretical framework for SDA 16 1.4.2. Characterization of a category and a class by a measure of discordance 18 1.4.3. Link between a characterization by the criteria W and the standard Tf-Idf 19 1.4.4. Ranking the individuals, the symbolic variables and the classes of a bar chart symbolic data table 21 1.5. Two directions of research 23 1.5.1. Parametrization of concordance and discordance criteria 23 1.5.2. Improving the explanatory power of any machine learning tool by a filtering process 25 1.6. Conclusion 27 1.7. References 28 Chapter 2. Likelihood in the Symbolic Context 31Richard EMILION and Edwin DIDAY 2.1. Introduction 31 2.2. Probabilistic setting 32 2.2.1. Description variable and class variable 32 2.2.2. Conditional distributions 33 2.2.3. Symbolic variables 33 2.2.4. Examples 35 2.2.5. Probability measures on (ℂ, C), likelihood 37 2.3. Parametric models for p = 1 38 2.3.1. LDA model 38 2.3.2. BLS method 41 2.3.3. Interval-valued variables 42 2.3.4. Probability vectors and histogram-valued variables 42 2.4. Nonparametric estimation for p = 1 45 2.4.1. Multihistograms and multivariate polygons 45 2.4.2. Dirichlet kernel mixtures 45 2.4.3. Dirichlet Process Mixture (DPM) 45 2.5. Density models for p ≥ 2 46 2.6. Conclusion 46 2.7. References 47 Chapter 3. Dimension Reduction and Visualization of Symbolic Interval-Valued Data Using Sliced Inverse Regression 49Han-Ming WU, Chiun-How KAO and Chun-houh CHEN 3.1. Introduction 49 3.2. PCA for interval-valued data and the sliced inverse regression 51 3.2.1. PCA for interval-valued data 51 3.2.2. Classic SIR 52 3.3. SIR for interval-valued data 53 3.3.1. Quantification approaches 54 3.3.2. Distributional approaches 56 3.4. Projections and visualization in DR subspace 58 3.4.1. Linear combinations of intervals 58 3.4.2. The graphical representation of the projected intervals in the 2D DR subspace 59 3.5. Some computational issues 61 3.5.1. Standardization of interval-valued data 61 3.5.2. The slicing schemes for iSIR 62 3.5.3. The evaluation of DR components 62 3.6. Simulation studies 63 3.6.1. Scenario 1: aggregated data 63 3.6.2. Scenario 2: data based on interval arithmetic 63 3.6.3. Results 64 3.7. A real data example: face recognition data 65 3.8. Conclusion and discussion 73 3.9. References 74 Chapter 4. On the “Complexity” of Social Reality. Some Reflections About the Use of Symbolic Data Analysis in Social Sciences 79Frédéric LEBARON 4.1. Introduction 79 4.2. Social sciences facing “complexity” 80 4.2.1. The total social fact, a designation of “complexity” in social sciences 80 4.2.2. Two families of answers 80 4.2.3. The contemporary deepening of the two approaches, “reductionist” and “encompassing” 81 4.2.4. Issues of scale and heterogeneity 82 4.3. Symbolic data analysis in the social sciences: an example 83 4.3.1. Symbolic data analysis 83 4.3.2. An exploratory case study on European data 83 4.3.3. A sociological interpretation 94 4.4. Conclusion 95 4.5. References 96 Part 2. Complex Data 99 Chapter 5. A Spatial Dependence Measure and Prediction of Georeferenced Data Streams Summarized by Histograms 101Rosanna VERDE and Antonio BALZANELLA 5.1. Introduction 101 5.2. Processing setup 103 5.3. Main definitions 104 5.4. Online summarization of a data stream through CluStream for Histogram data 106 5.5. Spatial dependence monitoring: a variogram for histogram data 107 5.6. Ordinary kriging for histogram data 110 5.7. Experimental results on real data 112 5.8. Conclusion 116 5.9. References 116 Chapter 6. Incremental Calculation Framework for Complex Data 119Huiwen WANG, Yuan WEI and Siyang WANG 6.1. Introduction 119 6.2. Basic data 122 6.2.1. The basic data space 122 6.2.2. Sample covariance matrix 123 6.3. Incremental calculation of complex data 124 6.3.1. Transformation of complex data 124 6.3.2. Online decomposition of covariance matrix 125 6.3.3. Adopted algorithms 128 6.4. Simulation studies 131 6.4.1. Functional linear regression 131 6.4.2. Compositional PCA 133 6.5. Conclusion 135 6.6. Acknowledgment 135 6.7. References 135 Part 3. Network Data 139 Chapter 7. Recommender Systems and Attributed Networks 141Françoise FOGELMAN-SOULIÉ, Lanxiang MEI, Jianyu ZHANG, Yiming LI, Wen GE, Yinglan LI and Qiaofei YE 7.1. Introduction 141 7.2. Recommender systems 142 7.2.1. Data used 143 7.2.2. Model-based collaborative filtering 145 7.2.3. Neighborhood-based collaborative filtering 145 7.2.4. Hybrid models 148 7.3. Social networks 150 7.3.1. Non-independence 150 7.3.2. Definition of a social network 150 7.3.3. Properties of social networks 151 7.3.4. Bipartite networks 152 7.3.5. Multilayer networks 153 7.4. Using social networks for recommendation 154 7.4.1. Social filtering 154 7.4.2. Extension to use attributes 155 7.4.3. Remarks 156 7.5. Experiments 156 7.5.1. Performance evaluation 156 7.5.2. Datasets 157 7.5.3. Analysis of one-mode projected networks 158 7.5.4. Models evaluated 160 7.5.5. Results 160 7.6. Perspectives 163 7.7. References 163 Chapter 8. Attributed Networks Partitioning Based on Modularity Optimization 169David COMBE, Christine LARGERON, Baptiste JEUDY, Françoise FOGELMAN-SOULIÉ and Jing WANG 8.1. Introduction 169 8.2. Related work 171 8.3. Inertia based modularity 172 8.4. I-Louvain 174 8.5. Incremental computation of the modularity gain 176 8.6. Evaluation of I-Louvain method 179 8.6.1. Performance of I-Louvain on artificial datasets 179 8.6.2. Run-time of I-Louvain 180 8.7. Conclusion 181 8.8. References 182 Part 4. Clustering 187 Chapter 9. A Novel Clustering Method with Automatic Weighting of Tables and Variables 189Rodrigo C. DE ARAÚJO, Francisco DE ASSIS TENORIO DE CARVALHO and Yves LECHEVALLIER 9.1. Introduction 189 9.2. Related Work 190 9.3. Definitions, notations and objective 191 9.3.1. Choice of distances 192 9.3.2. Criterion W measures the homogeneity of the partition P on the set of tables 193 9.3.3. Optimization of the criterion W 195 9.4. Hard clustering with automated weighting of tables and variables 196 9.4.1. Clustering algorithms MND–W and MND–WT 196 9.5. Applications: UCI data sets 201 9.5.1. Application I: Iris plant 201 9.5.2. Application II: multi-features dataset 204 9.6. Conclusion 206 9.7. References 206 Chapter 10. Clustering and Generalized ANOVA for Symbolic Data Constructed from Open Data 209Simona KORENJAK-ČERNE, Nataša KEJŽAR and Vladimir BATAGELJ 10.1. Introduction 209 10.2. Data description based on discrete (membership) distributions 210 10.3. Clustering 212 10.3.1. TIMSS – study of teaching approaches 215 10.3.2. Clustering countries based on age–sex distributions of their populations 217 10.4. Generalized ANOVA 221 10.5. Conclusion 225 10.6. References 226 List of Authors 229 Index 233

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Book SynopsisThis revised textbook motivates and illustrates the techniques of applied probability by applications in electrical engineering and computer science (EECS). The author presents information processing and communication systems that use algorithms based on probabilistic models and techniques, including web searches, digital links, speech recognition, GPS, route planning, recommendation systems, classification, and estimation. He then explains how these applications work and, along the way, provides the readers with the understanding of the key concepts and methods of applied probability. Python labs enable the readers to experiment and consolidate their understanding. The book includes homework, solutions, and Jupyter notebooks. This edition includes new topics such as Boosting, Multi-armed bandits, statistical tests, social networks, queuing networks, and neural networks. For ancillaries related to this book, including examples of Python demos and also Python labs used in Berkeley, please email Mary James at mary.james@springer.com. This is an open access book. Table of ContentsChapter 1. Page Rank - A.- Chapter 2. Page Rank - B.- Chapter 3. Multiplexing - A.- Chapter 4. Multiplexing - B.- Chapter 5. Networks - A.- Chapter 6. Networks - B.- Chapter 7. Digital Link - A.- Chapter 8. Digital Link - B.- Chapter 9. Tracking - A.- Chapter 10. Tracking - B.- Chapter 11. Speech Recognition - A.- Chapter 12. Speech Recognition - B.- Chapter 13. Route planning - A.- Chapter 14. Route Planning - B.- chapter 15. Perspective & Complements.- A. Elementary Probability.- B. Basic Probability.- . Index.

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Book SynopsisThis book shows the capabilities of Microsoft Excel in teaching marketing statistics effectively. It is a step-by-step, exercise-driven guide for students and practitioners who need to master Excel to solve practical marketing problems. If understanding statistics isn’t your strongest suit, you are not especially mathematically inclined, or if you are wary of computers, this is the right book for you.Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in marketing courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. Excel 2019 for Marketing Statistics: A Guide to Solving Practical Problems capitalizes on these improvements by teaching students and managers how to apply Excel to statistical techniques necessary in their courses and work.In this new edition, each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand marketing problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full practice test (with answers in an appendix) that allows readers to test what they have learned.Table of ContentsPreface.- Acknowledgements.- 1 Sample Size, Mean, Standard Deviation, and Standard Error of the Mean.- 2 Random Number Generator.- 3 Confidence Interval About the Mean Using the TINV Function and Hypothesis Testing.- 4 One-Group t-Test for the Mean.- 5 Two-Group t-Test of the Difference of the Means for Independent Groups.- 6 Correlation and Simple Linear Regression.- 7 Multiple Correlation and Multiple Regression.- 8 One-Way Analysis of Variance (ANOVA).- Appendix A: Answers to End-of-Chapter Practice Problems.- Appendix B: Practice Test.- Appendix C: Answers to Practice Test.- Appendix D: Statistical Formulas.- Appendix E: t-table.- Index.

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Book SynopsisThe main focus of this book is on presenting advances in fuzzy statistics, and on proposing a methodology for testing hypotheses in the fuzzy environment based on the estimation of fuzzy confidence intervals, a context in which not only the data but also the hypotheses are considered to be fuzzy. The proposed method for estimating these intervals is based on the likelihood method and employs the bootstrap technique. A new metric generalizing the signed distance measure is also developed. In turn, the book presents two conceptually diverse applications in which defended intervals play a role: one is a novel methodology for evaluating linguistic questionnaires developed at the global and individual levels; the other is an extension of the multi-ways analysis of variance to the space of fuzzy sets. To illustrate these approaches, the book presents several empirical and simulation-based studies with synthetic and real data sets. In closing, it presents a coherent R package called “FuzzySTs” which covers all the previously mentioned concepts with full documentation and selected use cases. Given its scope, the book will be of interest to all researchers whose work involves advanced fuzzy statistical methods.Table of Contents- 1. Introduction. - Part I Theoretical Part. - 2. Fundamental Concepts on Fuzzy Sets. - 3. Fuzzy Rule-Based Systems. - 4. Distances Between Fuzzy Sets. - 5. Fuzzy Random Variables and Fuzzy Distributions. - 6. Fuzzy Statistical Inference. - Conclusion Part I. - Part II Applications. - 7. Evaluation of Linguistic Questionnaire. - 8. Fuzzy Analysis of Variance. - Part III An R Package for Fuzzy Statistical Analysis: A DetailedDescription. - 9. FuzzySTs: Fuzzy Statistical Tools: A Detailed Description. - Conclusion.

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Book SynopsisNow in its second edition, this textbook provides an introduction to Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics.For this new edition, the introductory chapters on Python, data input and visualization have been reworked and updated. The chapter on experimental design has been expanded, and programs for the determination of confidence intervals commonly used in quality control have been introduced. The book also features a new chapter on finding patterns in data, including time series. A new appendix describes useful programming tools, such as testing tools, code repositories, and GUIs.The provided working code for Python solutions, together with easy-to-follow examples, will reinforce the reader’s immediate understanding of the topic. Accompanying data sets and Python programs are also available online. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis.With examples drawn mainly from the life and medical sciences, this book is intended primarily for masters and PhD students. As it provides the required statistics background, the book can also be used by anyone who wants to perform a statistical data analysis. Table of ContentsI Python and Statistics.- 1 Introduction.- 2 Python.- 3 Data Input.- 4 Data Display.- II Distributions and Hypothesis Tests.- 5 Basic Statistical Concepts.- 6 Distributions of One Variable.- 7 Hypothesis Tests.- 8 Tests of Means of Numerical Data.- 9 Tests on Categorical Data.- 10 Analysis of Survival Times.- III Statistical Modelling.- 11 Finding Patterns in Signals.- 12 Linear Regression Models.- 13 Generalized Linear Models.- 14 Bayesian Statistics.- Appendices.- A Useful Programming Tools.- B Solutions.- C Equations for Confidence Intervals.- D Web Ressources.- Glossary.- Bibliography.- Index.

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Book SynopsisBayes Factors for Forensic Decision Analyses with R provides a self-contained introduction to computational Bayesian statistics using R. With its primary focus on Bayes factors supported by data sets, this book features an operational perspective, practical relevance, and applicability—keeping theoretical and philosophical justifications limited. It offers a balanced approach to three naturally interrelated topics:Probabilistic Inference - Relies on the core concept of Bayesian inferential statistics, to help practicing forensic scientists in the logical and balanced evaluation of the weight of evidence.Decision Making - Features how Bayes factors are interpreted in practical applications to help address questions of decision analysis involving the use of forensic science in the law.Operational Relevance - Combines inference and decision, backed up with practical examples and complete sample code in R, including sensitivity analyses and discussion on how to interpret results in context.Over the past decades, probabilistic methods have established a firm position as a reference approach for the management of uncertainty in virtually all areas of science, including forensic science, with Bayes' theorem providing the fundamental logical tenet for assessing how new information—scientific evidence—ought to be weighed. Central to this approach is the Bayes factor, which clarifies the evidential meaning of new information, by providing a measure of the change in the odds in favor of a proposition of interest, when going from the prior to the posterior distribution. Bayes factors should guide the scientist's thinking about the value of scientific evidence and form the basis of logical and balanced reporting practices, thus representing essential foundations for rational decision making under uncertainty.This book would be relevant to students, practitioners, and applied statisticians interested in inference and decision analyses in the critical field of forensic science. It could be used to support practical courses on Bayesian statistics and decision theory at both undergraduate and graduate levels, and will be of equal interest to forensic scientists and practitioners of Bayesian statistics for driving their evaluations and the use of R for their purposes.This book is Open Access.Table of ContentsPart I - Introduction to the Bayes Factor (Likelihood Ratio)Presents the principal statistic discussed throughout this book: the Bayes factor, in the context of forensic science, more often known as the likelihood ratio. Subsections of this part: clarify the different roles (known as, respectively, the ‘investigative’ and ‘evaluative’ role) that forensic scientists may assume in their daily work articulate the reasons why forensic scientists should adhere to a Bayesian framework of inference in order to ensure coherence in their inferential and decision-making tasks formally describe what the Bayes factor is and how it relates to coherent decision analysis describe the advantages that Bayes factors offer in assessing, articulating and communicating the value of scientific evidence in general, and in legal proceedings in particular Part II - Bayes Factor for Investigative PurposesDeals with a peculiar task of the forensic scientist, known as the ‘investigative mode’ (i.e., one of the two main modes of functioning introduced in Part I). That is, in forensic settings, it may well be the case that a potential source (i.e., a suspect) is not available for comparative purposes, in particular in early stages of the legal process. Notwithstanding, data and measurements on recovered material (e.g., seized on a crime scene) can be used for an investigative purpose. In this mode of working, scientists can offer to investigative authorities (or, in a more general perspective, mandating parties) information to help discriminate between general propositions concerning, for instance, the characterizing features of the source that left the recovered material (e.g., gender, externally visible traits such as hair and eye color, handedness, etc.). At this stage in the process, the scientist tries to help answer questions such as ‘what happened?’ in the case under investigation, or ‘what can we infer about the offender?’. In this context, the Bayes factor can be used as a statistic to measure and help decide how to classify, for example, objects and substances on which measurements have been made. This use of the Bayes factor will be explained through practical examples involving topics such as handwriting characteristics, toner from printers in questioned document examination, drugs of abuse, toxicology, forensic anthropology and forensic DNA profiling (listing is not exhaustive and may evolve during the writing of the book). Both univariate and multivariate data will be considered, with or without replicates, and involving different statistical distributions (i.e. Binomial, Poisson, Normal, etc.). The examples refer to realistic forensic applications as they may be encountered in judicial contexts and the forensic practitioner’s own field of activity. Data will be selected from published literature or from the author’s own records. R sample code will be specified and explanations will be included on how to interpret results in context and convey their meaning appropriately.Part III - Bayes Factor for Evaluative PurposesFocuses on the scientist’s role in a more advanced stage of the legal process. That is, situations in which the evaluation of scientific findings will take into account a potential source of the recovered material (e.g., a suspect or an object/tool). This kind of reporting is typically required when scientists need to communicate their results for use at trial. It is of utmost importance at this juncture that scientists express the value of the observed data and findings under competing hypotheses, focusing on a potential (i.e., known) source versus an alternative source (e.g., propositions such as ‘the recovered item comes from the same source as the control material’, and ‘the recovered item is from a source that is different from that of the control material’). The Bayes factor is the central inferential concept for such expressions of weight of evidence. In this part of the book, too, examples will be chosen with the intention to reflect realistic scenarios as they may arise in current judicial practice. In particular, the outline will consider uni- and multi­-variate data from scenarios related to microtraces (e.g., glass and paint fragments), handwriting and drugs of abuse. Besides computational R code, this chapter will also include (i) sensitivity analyses to provide readers with a means to further investigate the properties of the proposed evaluative procedures based on the Bayes factor, and (ii) decision theoretic extensions to outline how to interface expressions of weight of evidence with the broader perspective of coherent decision-making. Part IV - ConclusionSummarizes the key messages developed throughout this book, emphasizing (i) the contribution of an extended use of the Bayes factor in a normative decision framework, and (ii) the role of the Bayes factor as the relevant statistic for both investigative and evaluative tasks that characterize current forensic science.

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Book SynopsisThis open access book provides insights into the novel Locally Refined B-spline (LR B-spline) surface format, which is suited for representing terrain and seabed data in a compact way. It provides an alternative to the well know raster and triangulated surface representations. An LR B-spline surface has an overall smooth behavior and allows the modeling of local details with only a limited growth in data volume. In regions where many data points belong to the same smooth area, LR B-splines allow a very lean representation of the shape by locally adapting the resolution of the spline space to the size and local shape variations of the region. The iterative method can be modified to improve the accuracy in particular domains of a point cloud. The use of statistical information criterion can help determining the optimal threshold, the number of iterations to perform as well as some parameters of the underlying mathematical functions (degree of the splines, parameter representation). The resulting surfaces are well suited for analysis and computing secondary information such as contour curves and minimum and maximum points. Also deformation analysis are potential applications of fitting point clouds with LR B-splines.Table of ContentsIntroduction.- Locally Refined Splines.- Adaptive surface Fitting with Local Refinement: LR B-spline Surfaces.- A Statistical Criterion to Judge the Goodness of Fit of LR B-splines Surface Approximation.- LR B-splines for Representation of Terrain and Seabed: Data Fusion, Outliers, and Voids.- LR B-spline Surfaces and Volumes for Deformation Analysis of Terrain Data.- Conclusion.

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Book SynopsisThis book is a self-guided tour of MATLAB for engineers and life scientists. It introduces the most commonly used programming techniques through biologically inspired examples. Although the text is written for undergraduates, graduate students and academics, as well as those in industry, will find value in learning MATLAB. The book takes the emphasis off of learning syntax so that the reader can focus more on algorithmic thinking. Although it is not assumed that the reader has taken differential equations or a linear algebra class, there are short introductions to many of these concepts. Following a short history of computing, the MATLAB environment is introduced. Next, vectors and matrices are discussed, followed by matrix-vector operations. The core programming elements of MATLAB are introduced in three successive chapters on scripts, loops, and conditional logic. The last three chapters outline how to manage the input and output of data, create professional quality graphics and find and use MATLAB toolboxes. Throughout, biomedical and life science examples are used to illustrate MATLAB's capabilities.Table of ContentsIntroduction.- MATLAB Programming Environment.- Vectors.- Matrices.- MatrixVector Operations.- Scripts and Functions.- Loops.- Conditional Logic.- Data In/Data Out.- Graphics.- Toolboxes.

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Book SynopsisThis textbook provides a concise introduction to optimal experimental design and efficiently prepares the reader for research in the area. It presents the common concepts and techniques for linear and nonlinear models as well as Bayesian optimal designs. The last two chapters are devoted to particular themes of interest, including recent developments and hot topics in optimal experimental design, and real-world applications. Numerous examples and exercises are included, some of them with solutions or hints, as well as references to the existing software for computing designs. The book is primarily intended for graduate students and young researchers in statistics and applied mathematics who are new to the field of optimal experimental design. Given the applications and the way concepts and results are introduced, parts of the text will also appeal to engineers and other applied researchers.Table of ContentsPreface.- Motivating Introduction.- Linear Models.- Nonlinear Models.- Bayesian Optimal Designs.- Hot Topics.- Real Case Examples.- Appendices.- References.- Index.

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Book SynopsisThis book illustrates the capabilities of Microsoft Excel to teach applied statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical statistical problems in industry. If understanding statistics isn’t your strongest suit, you are not especially mathematically-inclined, or if you are wary of computers, this is the right book for you. Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in statistics courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past.The 2nd edition of Applied Business Statistics for Business and Management capitalizes on these improvements by teaching students and practitioners how to apply Excel to statistical techniques necessary in their courses and workplace. Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand business problems. Practice problems are provided at the end of each chapter with their solutions.Table of Contents​Statistics and Data.- Summarizing Data.- Descriptive Statistics and Graphing.- Normal World.- Survey Design.- Sampling.- Inference.- Probability.- Correlation.- Simple Linear Regression.- Multiple Regression.- Significance Tests.- Non Linear Regression.- Survey Reports​.

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Book SynopsisThis guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information. Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc., can be a painstakingly laborious process. Roughly 80% of data analysis is spent on cleaning and preparing data; however, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it is essential that one become fluent and efficient in data wrangling techniques.This book will guide the user through the data wrangling process via a step-by-step tutorial approach and provide a solid foundation for working with data in R. The author's goal is to teach the user how to easily wrangle data in order to spend more time on understanding the content of the data. By the end of the book, the user will have learned: How to work with different types of data such as numerics, characters, regular expressions, factors, and dates The difference between different data structures and how to create, add additional components to, and subset each data structure How to acquire and parse data from locations previously inaccessible How to develop functions and use loop control structures to reduce code redundancy How to use pipe operators to simplify code and make it more readable How to reshape the layout of data and manipulate, summarize, and join data sets Table of Contents1. Preface 2. Introduction a. The Role of Data Wrangling i. Introduction to R 1. Open Source 2. Flexibility 3. Community ii. R Basics 1. Assignment & Evaluation 2. Vectorization 3. Getting help 4. Workspace 5. Working with packages 6. Style guide 3. Working with Different Types of Data in R a. Dealing with Numbers i. Integer vs. Double ii. Generating sequence of non-random numbers iii. Generating sequence of random numbers iv. Setting the seed for reproducible random numbers v. Comparing numeric values vi. Rounding numbers b. Dealing with Character Strings i. Character string basics ii. String manipulation with base R iii. String manipulation with stringr iv. Set operatons for character strings c. Dealing with Regular Expressions i. Regex Syntax ii. Regex Functions iii. Additional resources d. Dealing with Factors i. Creating, converting & inspecting factors ii. Ordering levels iii. Revalue levels iv. Dropping levels e. Dealing with Dates i. Getting current date & time ii. Converting strings to dates iii. Extract & manipulate parts of dates iv. Creating date sequences v. Calculations with dates vi. Dealing with time zones & daylight savings vii. Additional resources <4. Managing Data Structures in R a. Data Structure Basics i. Identifying the Structure ii. Attributes b. Managing Vectors i. Creating ii. Adding on to iii. Adding attributes iv. Subsetting c. Managing Lists i. Creating iii. Adding attributes iv. Subsetting d. Managing Matrices i. Creating ii. Adding on to iii. Adding attributes iv. Subsetting e. Managing Data Frames i. Creating ii. Adding on to iii. Adding attributes iv. Subsetting f. Dealing with Missing Values i. Testing for missing values ii. Recoding missing values iii. Excluding missing values 5. Importing, Scraping, and Exporting Data with R a. Importing Data i. Reading data from text files ii. Reading data from Excel files iii. Load data from saved R object file iv. Additional resources b. Scraping Data i. Importing tabular and Excel files stored online ii. Scraping HTML text iii. Scraping HTML table data iv. Working with APIs v. Additional Resources c. Exporting Data i. Writing data to text files ii. Writing data to Excel files iii. Saving data as an R object file iv. Additional resources 6. Creating Efficient & Readable Code in R a. Functions i. Function Components ii. Arguments iii. Scoping Rules iv. Lazy Evaluation v. Returning Multiple Outputs from a Function vi. Dealing with Invalid Parameters vii. Saving and Sourcing Functions viii. Additional Resources b. Loop Control Statements i. Basic control statements (i.e. if, for, while, etc.) ii. Apply family iii. Other useful “loop-like” functions iv. Additional Resources c. Simplify Your Code with %>% i. Pipe (%>%) Operator ii. Additional Functions iii. Additional Pipe Operators iv. Additional Resources 7. Shaping & Transforming Your Data with R a. Reshaping Your Data with tidyr i. Making wide data long ii. Making long data wide iii. Splitting a single column into multiple columns iv. Combining multiple columns into a single column v. Additional tidyr functions vi. Sequencing your tidyr operations vii. Additional resources b. Transforming Your Data with dplyr i. Selecting variables of interest ii. Filtering rows iii. Grouping data by categorical variables iv. Performing summary statistics on variables v. Arranging variables by value vi. Joining datasets vii. Creating new variables viii. Additional resources

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Book SynopsisDieses Lehrbuch enthält in kompakter, übersichtlicher Form die wichtigsten finanzmathematischen Fragestellungen und die dazu passenden Prozeduren von MATLAB (Erklärung der Ein- und Ausgabegrößen, mathematische Darstellung des entsprechenden finanztechnischen Vorgangs, Parameterwahlmöglichkeiten). Damit werden sowohl die numerische als auch die grafische Realisierung von Aufgaben- und Problemstellungen der Finanzmathematik in effektiver Weise ermöglicht. Table of ContentsEinführung in MATLAB - Datumfunktionen - Abschreibungen - Analyse von Cash Flows - Rentenrechnung - Tilgungsrechnung - Analyse festverzinslicher Wertpapiere (Anleihen / Bonds) - Portfolio-Optimierung - Analyse von Finanzderivaten (Optionen) - Finanz-Zeitreihen (Volatilitätsanalyse) - Verzeichnis der MATLAB-Prozeduren

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Book SynopsisDifferentialgleichungen spielen in den Naturwissenschaften und der Technik eine bedeutende Rolle, da viele Modelle mit ihrer Hilfe formuliert werden. Für die exakte Lösung dieser Gleichungen gibt es ausgefeilte mathematische Methoden, die in dem Computeralgebra-System Mathematica verfügbar sind. Das Buch enthält einerseits eine Einführung in die Theorie der gewöhnlichen und partiellen Differentialgleichungen und beschreibt andererseits, wie sich Mathematica zur Lösung dieser Gleichungen einsetzen läßt. Die theoretischen Ergebnisse werden in algorithmischer Form angegeben und mit vielen Beispielen ergänzt, die auch die graphischen Fähigkeiten von Mathematica ausnutzen.Table of ContentsDifferentialgleichungen erster Ordnung - Differentialgleichungssysteme erster Ordnung - Lineare Differentialgleichungen mit konstanten Koeffizienten - Partielle Differentialgleichungen erster Ordnung - Lineare Partielle Differentialgleichungen zweiter Ordnung

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Book SynopsisThis book presents the statistical analysis of compositional data sets, i.e., data in percentages, proportions, concentrations, etc. The subject is covered from its grounding principles to the practical use in descriptive exploratory analysis, robust linear models and advanced multivariate statistical methods, including zeros and missing values, and paying special attention to data visualization and model display issues. Many illustrated examples and code chunks guide the reader into their modeling and interpretation. And, though the book primarily serves as a reference guide for the R package “compositions,” it is also a general introductory text on Compositional Data Analysis. Awareness of their special characteristics spread in the Geosciences in the early sixties, but a strategy for properly dealing with them was not available until the works of Aitchison in the eighties. Since then, research has expanded our understanding of their theoretical principles and the potentials and limitations of their interpretation. This is the first comprehensive textbook addressing these issues, as well as their practical implications with regard to software.The book is intended for scientists interested in statistically analyzing their compositional data. The subject enjoys relatively broad awareness in the geosciences and environmental sciences, but the spectrum of recent applications also covers areas like medicine, official statistics, and economics. Readers should be familiar with basic univariate and multivariate statistics. Knowledge of R is recommended but not required, as the book is self-contained.Trade ReviewFrom the reviews:“This book offers not only the theoretical background to analyse and interpret compositional data, but also the R support and guidance for the compositions package. The book is organised in 7 chapters. … The book is built in an accessible manner for undergraduates and postgraduates alike and offers an all in one overview of the analysis of compositional data in R.” (Irina Ioana Mohorianu, zbMATH, Vol. 1276, 2014)Table of ContentsIntroduction.- Fundamental Concepts of Compositional Data Analysis.- Distributions for Random Compositions.- Descriptive Analysis of Compositional Data.- Linear Models for Compositions.- Multivariate Statistics.- Zeroes, Missings and Outliers.- References.- Index.

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Book SynopsisWie können große und kleine Datenmengen aus Beobachtungen, Messungen, Befragungen, Untersuchungen, Analysen etc. verwaltet, aufbereitet, komprimiert, mit Kennzahlen erklärt und wirksam grafisch dargestellt werden? Wie kann man dazu Hypothesen prüfen, Zusammenhänge aufdecken, Abhängigkeiten finden? Dieses Buch zeigt Ihnen, wie die grundlegenden Methoden der Statistik recht einfach mit Excel umsetzbar sind. Es wurden in einheitlicher, sehr verständlicher Methodik die grundlegenden statistischen Verfahren sowohl der beschreibenden als auch der beurteilenden Statistik zusammengestellt. Umfangreiche Beispiele, didaktisch aufbereitet und stets ausführlich mit Excel umgesetzt, bieten eine umfassende Hilfe für den Umgang mit Datenmengen.Alle Beispiele stehen online für individuelle Übungen bereit. Trade Review“... Die Wahl geeigneter Beispiele und viele Abbildungen ... machen das Buch zu einer Empfehlung für alle, die einen verständlichen Grundkurs Statistik mit Excel suchen ...” (Karl Schäfer, in: Amazon.de, 19. Juli 2016)Table of ContentsWas man über Microsoft Excel wissen sollte.- Excel und große Datenmengen.- Beschreibende Statistik – Auskünfte über eine Da­tenreihe.- Beschreibende Statistik – Auskünfte über mehrere Da­tenreihen.- Zufall, Wahrscheinlichkeit, Verteilungsfunktionen.- Beurteilende Statistik – Prüfen von Verteilungen.- Beurteilende Statistik – Parameterprüfung mit einer Stichprobe.- Beurteilende Statistik – Parametervergleiche zweier verbundener Stichproben.- Beurteilende Statistik – Parametervergleiche zweier nicht verbundener Stichproben.- Einfache Varianzanalyse nicht verbundener Stichproben - Schätzungen.

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Book SynopsisComputeralgebra- Systeme wie MAPLE gehören heute zum Alltag aller, die Mathematik in Schule, Wirtschaft und Hochschule anwenden. Gleichzeitig bieten sie die Möglichkeit, in ganz anderer Weise Beispiele zu untersuchen und zu veranschaulichen, als dies mit Bleistift und Papier möglich ist. Neben einer Einführung in MAPLE hat dieses Buch zum Ziel, durch die Behandlung von Beispielen den Stoff des ersten Studienjahres, wie er in den Vorlesungen zur Analysis und Linearen Algebra behandelt wird, zu vertiefen und zu veranschaulichen. Es besteht aus Aufgaben mit Erläuterungen, anhand derer der Leser den Stoff eigenständig durcharbeiten soll. Mathematische Anwendersysteme als berufsbildende Kompetenz in der Bachelor-Ausbildung: Das Buch eignet sich für ein Modul aufbauend auf den Grundvorlesungen Analysis und Lineare Algebra. Materialien zu diesem Buch für das E-Learning System OKUSON werden für Dozenten unter OnlinePLUS bereitgestellt.Table of ContentsEinführung in MAPLE - Erste Beispiele und Aufgaben - Elementare Operationen mit Matrizen und Vektoren - Das Gauß-Verfahren und die Cramersche Regel - Diagonalisierbarkeit komplexer Matrizen - Matrizen mit positiven Einträgen - Reelle Funktionen einer Variablen - Taylor-Entwicklung - Reelle Funktionen von mehreren Variablen - Quadratische Gleichungen und Quadriken - Hermite-Polynome und Fourier-Reihen - Normalformen - Gewöhnliche Differentialgleichungen - Lösungen

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Book SynopsisIl volume fornisce un'introduzione a teoria e applicazioni dei modelli lineari generalizzati. Si presentano modelli di regressione per risposte continue, binarie, categoriali e di conteggio. Si offre anche un'introduzione ai modelli per risposte correlate. Utilizzando il software statistico R, vengono forniti gli strumenti per l'analisi dei dati tramite i diversi modelli parametrici e semiparametrici. Gli esempi con R alla fine di ciascun capitolo rappresentano una guida ad esercitazioni con il computer e richiedono una partecipazione attiva nello svolgere le analisi proposte. Numerosi esercizi concludono ogni capitolo. Il taglio adottato è funzionale ad approfondire in modo integrato aspetti teorici e applicativi. Unico nel suo genere, è rivolto agli studenti di Scienze Statistiche. Table of Contents1. Modelli lineari e lineari generalizzati.- 2. Modelli lineari generalizzati.- 3. Modelli per dati bancari.- 4. Modelli per risposte politomiche.- 5. Modelli per dati di conteggio.- 6. Quasi-verosimiglianza.- Modelli per risposte correlate.- A Dati utilizzati nel testo.- B Distribuzioni di probabilità.- C Eguaglianza tra stime OLS e GLS.- D Il metodo delta.- E Funzioni generatrici.- F Codice R per l’esempio 2.9.- G Equivalenza tra residui di Pearson e di devianza.- H Modelli per la sovradispersione: schema.

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Book SynopsisIl libro contiene in forma compatta il programma svolto negli insegnamenti introduttivi di Statistica e tratta alcuni argomenti indispensabili per l'attività di ricerca, come le tecniche di simulazione Monte Carlo, i metodi di inferenza statistica, di best fit e di analisi dei dati di laboratorio. Gli argomenti vengono sviluppati partendo dai fondamenti, evidenziandone gli aspetti applicativi, fino alla descrizione dettagliata di molti casi di particolare rilevanza in ambito scientifico e tecnico. Il testo è rivolto agli studenti universitari dei corsi ad indirizzo scientifico e a tutti quei ricercatori che devono risolvere problemi concreti che coinvolgono l’analisi dei dati e le tecniche di simulazione. In questa edizione, completamente rivista e corretta, sono stati aggiunti alcuni importanti argomenti sul test d’ipotesi (a cui è stato dedicato un capitolo interamente nuovo) e sul trattamento degli errori sistematici. Per la prima volta è stato adottato il software R, con una ricca libreria di programmi originali accessibile al lettore.Table of Contents1 La probabilità.- 2 Rappresentazione dei fenomeni aleatori.- 3 Calcolo elementare delle probabilità.- 4 Calcolo delle probabilità per più variabili.- 5 Funzioni di variabili aleatorie.- 6 Statistica di base: stime.- 7 Statistica di base: verifica di ipotesi.- 8 Il metodo Monte Carlo.- 9 Applicazioni del metodo Monte Carlo.- 10 Inferenza statistica e verosimiglianza.- 11 Minimi quadrati.- 12 Analisi dei dati sperimentali.

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Book SynopsisThis book discusses all major topics on survey sampling and estimation. It covers traditional as well as advanced sampling methods related to the spatial populations. The book presents real-world applications of major sampling methods and illustrates them with the R software. As a large sample size is not cost-efficient, this book introduces a new method by using the domain knowledge of the negative correlation between the variable of interest and the auxiliary variable in order to control the size of a sample. In addition, the book focuses on adaptive cluster sampling, rank-set sampling and their applications in real life. Advance methods discussed in the book have tremendous applications in ecology, environmental science, health science, forestry, bio-sciences, and humanities. This book is targeted as a text for undergraduate and graduate students of statistics, as well as researchers in various disciplines.Table of Contents-1. Introduction.- 2. Simple Random Sampling.- 3. Stratied Random Sampling.- 4. Cluster Sampling.- 5. Double Sampling.- 6. Probability Proportional to Size Sampling.- 7. Systematic Sampling.- 8. Resampling Techniques.- 9. Adaptive Cluster Sampling.- 10. Two-Stage Adaptive Cluster Sampling.- 11. Adaptive Cluster Double Sampling.- 12. Inverse Adaptive Cluster Sampling.- 13. Two Stage Inverse Adaptive Cluster Sampling.- 14. Stratified Inverse Adaptive Cluster Sampling.- 15. Negative Adaptive Cluster Sampling.- 16. Negative Adaptive Cluster Double Sampling.- 17. Two- Stage Negative Adaptive Cluster Sampling.- 18. Balanced and Unbalanced Ranked Set Sampling.- 19. Ranked Set Sampling in Other Parameter Estimation and Non-Parametric Inference.- 20. Important Versions of Ranked Set Sampling.- 21. Sampling Errors.

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Book SynopsisThis book provides a highly practical introduction to Bayesian statistical modeling with Stan, which has become the most popular probabilistic programming language.The book is divided into four parts. The first part reviews the theoretical background of modeling and Bayesian inference and presents a modeling workflow that makes modeling more engineering than art. The second part discusses the use of Stan, CmdStanR, and CmdStanPy from the very beginning to basic regression analyses. The third part then introduces a number of probability distributions, nonlinear models, and hierarchical (multilevel) models, which are essential to mastering statistical modeling. It also describes a wide range of frequently used modeling techniques, such as censoring, outliers, missing data, speed-up, and parameter constraints, and discusses how to lead convergence of MCMC. Lastly, the fourth part examines advanced topics for real-world data: longitudinal data analysis, state space models, spatial data analysis, Gaussian processes, Bayesian optimization, dimensionality reduction, model selection, and information criteria, demonstrating that Stan can solve any one of these problems in as little as 30 lines.Using numerous easy-to-understand examples, the book explains key concepts, which continue to be useful when using future versions of Stan and when using other statistical modeling tools. The examples do not require domain knowledge and can be generalized to many fields. The book presents full explanations of code and math formulas, enabling readers to extend models for their own problems. All the code and data are on GitHub.Table of ContentsPrefacePart I: Introduction Chapter 1: Overview of Statistical Modeling and StanChapter 2: Review of Bayesian InferenceChapter 3: Before Starting Statistical ModelingPart II: Introduction of StanChapter 4: Start with Stan, RStan and PyStanChapter 5: Elementary Regression and Model CheckPart III: Essential Components and Techniques for ExpertsChapter 6: Introduction of Distributions from Modeling ViewpointsChapter 7: Issues of RegressionChapter 8: Nonlinear ModelChapter 9: Hierarchical ModelChapter 10: Advanced GrammarsChapter 11: How to Lead ConvergenceChapter 12: Discrete ParametersChapter 13: Usage of MCMC SamplesPart IV: Advanced Topics for Real-world DataChapter 14: Longitudinal Data Analysis with State Space Model Chapter 15: Spatial Data Analysis with Markov Field ModelChapter 16: Survival AnalysisChapter 17: Causal InferenceChapter 18: Model selectionAppendix: Differences between Stan and BUGSReferenceIndex

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Book SynopsisThis book provides a design and development perspective MPC for micro-grid control, emphasizing step-by-step conversion of a nonlinear MPC to linear MPC preserving critical aspects of nonlinear MPC. The book discusses centralized and decentralized MPC control algorithms for a generic modern-day micro-grid consisting of vital essential constituents. It starts with the nonlinear MPC formulation for micro-grids. It also moves towards the linear time-invariant and linear time-variant approximations of the MPC for micro-grid control. The contents also discuss how the application of orthonormal special functions can improve computational complexity of MPC algorithms. It also highlights various auxiliary requirements like state estimator, disturbance compensator for robustness, selective harmonic eliminator for eliminating harmonics in the micro-grid, etc. These additional requirements are crucial for the successful online implementation of the MPC. In the end, the book shows how a well-designed MPC is superior in performance compared to the conventional micro-grid primary controllers discussed above. The key topics discussed in this book include – the detailed modeling of micro-grid components; operational modes in micro-grid and their control objectives; conventional micro-grid primary controllers; the importance of MPC as a micro-grid primary controller; understanding of MPC operation; nonlinear MPC formulation; linear approximations of MPC; application of special functions in the MPC formulation; and other online requirements for the MPC implementation. The examples in the book are available both from a calculation point of view and as MATLAB codes. This helps the students get acquainted with the subject first and then allows them to implement the subject they learn in software for further understanding and research.Table of ContentsChapter 1. Micro-grid Introduction and Overview.- Chapter 2. An Overview of Micro-grid Control.- Chapter 3. Mathematical Modelling of a Micro-grid.- Chapter 4. Introduction to Model Predictive Control.- Chapter 5. LTI-MPC for the Micro-grid Control.- Chapter 6. LTV-MPC with Extended “TAIL”.- Chapter 7. Special functions in the MPC formulation.- Chapter 8. Auxiliary Requirements for Real-time Implementation.- Chapter 9. Conclusion and Future Scope.

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Book SynopsisStatistical Programming in SAS Second Edition provides a foundation for programming to implement statistical solutions using SAS, a system that has been used to solve data analytic problems for more than 40 years. The author includes motivating examples to inspire readers to generate programming solutions. Upper-level undergraduates, beginning graduate students, and professionals involved in generating programming solutions for data-analytic problems will benefit from this book. The ideal background for a reader is some background in regression modeling and introductory experience with computer programming.The coverage of statistical programming in the second edition includes Getting data into the SAS system, engineering new features, and formatting variables Writing readable and well-documented code Structuring, implementing, and debugging programs that are well documented Creating solutions to novel problems Combining dataTrade Review"This book is useful for people who want to learn SAS programing, and assumes the students have knowledge of multiple linear regression and one-way ANOVA models.…The second edition has added a chapter on text processing, and reorganized the chapter order…Some topics that are relevant for the SAS Base and Certifications exams are covered, and a nice feature is the highlighting of programing tips in gray." ~Technometrics"This is a very complete book for programming SAS in statistical analyses. This second edition offers the possibility to debug some programs and provides new examples and applications, which are very useful. This book is a very useful companion tool for students or beginners in SAS, or for more experienced statisticians who already use SAS for statistical analyses."~ISCB NewsTable of ContentsContentsPreface ..............................................................................................................................................ixAcknowledgments ...................................................................................................................... xiiiAuthor .............................................................................................................................................xv1. Structuring, Implementing, and Debugging Programs to Learn about Data ...........11.1 Statistical Programming ................................................................................................11.2 Learning from Constructed, Artificial Data ...............................................................2Processing a Particular Data Set—Extracting Variable Names from aColumn of an Input Data Set.........................................................................................2Learning More about Unfamiliar Statistical Methods—Linear MixedEffects Models .................................................................................................................5Improving Your Intuition about Statistical Theory— Sampling Distributionof Means ...........................................................................................................................81.3 Good Programming Practice ...................................................................................... 11Document Your Programs! .......................................................................................... 11Use Meaningful Variable Names ................................................................................ 13Use a Variety of CaSeS in Program Statements ........................................................ 14Indent Program Statements That Naturally Go Together ....................................... 141.4 SAS Program Structure ................................................................................................ 151.5 What Is a SAS Data Set? ............................................................................................... 211.6 Internally Documenting SAS Programs ....................................................................221.7 Basic Debugging ...........................................................................................................231.8 Getting Help ..................................................................................................................27Using Help in SAS ........................................................................................................27Getting Help from a Web Browser Search .................................................................291.9 Exercises .........................................................................................................................292. Reading, Creating, and Formatting Data Sets ................................................................ 312.1 What Does a SAS DATA Step Do? .............................................................................. 312.2 Reading Data from External Files ..............................................................................33Reading Data Directly as Part of a Program—Anyone for Datalines? .................34Reading Data Sets Saved as Text—INFILE Can Be Your Friend (PROCIMPORT Too!) ................................................................................................................38Sometimes, Variables Are in Particular Columns or in Particular Formats .........402.3 Reading CSV, Excel, and TEXT Files .......................................................................... 412.4 Temporary versus Permanent Status of Data Sets ...................................................432.5 Formatting and Labeling Variables ............................................................................46Using Formats to Read and Display Variable Values ..............................................46Internal Representations and Output Displays ........................................................49Character, Numeric, Time, and Date Formats ..........................................................532.6 User-Defined Formatting .............................................................................................58Saving Formats for Later Use ......................................................................................632.7 Recoding and Transforming Variables in a DATA Step ........................................66Indicator Variables ......................................................................................................682.8 Writing Out a File or Making a Simple Report ......................................................73Simple Report Generation .........................................................................................73Exporting a File ...........................................................................................................772.9 Exercises .......................................................................................................................803. Programming a DATA Step ................................................................................................833.1 Writing Programs by Subdividing Tasks ................................................................83Estimate the Probability That a Randomly Selected 30- to 39-Year-OldMale Is Taller than a Randomly Selected Female of the Same Age .....................83Conditional Execution ...........................................................................................84Looping to Repeat a Task ......................................................................................86Returning to the Height Probability Simulation ............................................... 873.2 Ordering How Tasks Are Done ................................................................................90Missing Data in Functions .........................................................................................923.3 Indexable Lists of Variables (Also Known as Arrays) ...........................................93Defining Values in the Variable List .........................................................................93Inputting Values in the Variable List ........................................................................94Reassign Missing Value Codes for Numeric Variables “.” ...................................95Recoding Missing Values for All Numeric and Character Variables ..................953.4 Functions Associated with Statistical Distributions .............................................963.5 Generating Variables Using Random Number Generators ................................ 1023.6 Remembering Variable Values across Observations ........................................... 105Processing Multiple Observations for a Single Observation .............................. 1063.7 Case Study 1: Is the Two-Sample t-Test Robust to Violations of theHeterogeneous Variance Assumption? ................................................................. 109Case Study 1 (Revisited with DATA Step Programming) .................................. 1183.8 Efficiency Considerations—How Long Does It Take? .........................................1223.9 Case Study 2: Monte Carlo Integration to Estimate an Integral ........................ 1233.10 Case Study 3: Simple Percentile-Based Bootstrap ................................................ 1283.11 Case Study 4: Randomization Test for the Equality of Two Populations ......... 1303.12 Exercises ..................................................................................................................... 1344. Combining, Extracting, and Reshaping Data ............................................................... 1374.1 Adding Observations by SET-ing Data Sets.......................................................... 1374.2 Adding Variables by MERGE-ing Data Sets ......................................................... 1404.3 Working with Tables in PROC SQL ....................................................................... 1484.4 Converting Wide to Long Formats ......................................................................... 1614.5 Converting Long to Wide Formats ......................................................................... 1644.6 Case Study: Reshaping a World Bank Data Set .................................................... 1664.7 Building Training and Validation Data Sets ......................................................... 1754.8 Exercises ..................................................................................................................... 1794.9 Self-study Lab ............................................................................................................ 1805. Macro Programming .......................................................................................................... 1915.1 What Is a Macro and Why Would You Use It? ..................................................... 1915.2 Motivation for Macros: Numerical Integration to DetermineP(0 < Z < 1.645) ......................................................................................................... 1915.3 Processing Macros .................................................................................................... 1955.4 Macro Variables, Parameters, and Functions........................................................ 1955.5 Conditional Execution, Looping, and Macros ...................................................... 198More Complicated Macro Variable Construction ................................................203Changing Locations in a Macro during Execution ..............................................2045.6 Debugging Macro Code and Programs.................................................................206Write Out Values of Macro Variables .....................................................................206Useful SAS Options for Debugging Macros ......................................................... 2075.7 Saving Macros ........................................................................................................... 2115.8 Functions and Routines for Macros ....................................................................... 2115.9 Case Study: Macro for Constructing Training and Test Data Set for ModelComparison ............................................................................................................... 2165.10 Case Study: Processing Multiple Data Sets ...........................................................2235.11 Exercises .....................................................................................................................2276. Customizing Output and Generating Data Visualizations .......................................2296.1 Using the Output Delivery System ........................................................................229Basic Ideas ..................................................................................................................229Destinations—RTF, HTML, PDF, and More! .........................................................230What’s Produced and How to Select It ..................................................................235Another Destination That Stat Programmers Should Visit—OUTPUT ............ 2436.2 Graphics in SAS ......................................................................................................... 2496.3 ODS Statistical Graphics ..........................................................................................2506.4 Modifying Graphics Using the ODS Graphics Editor ......................................... 2576.5 Graphing with Styles and Templates .....................................................................2606.6 Statistical Graphics—Entering the Land of SG Procedures ............................... 266SGPLOT ...................................................................................................................... 266SGPANEL ................................................................................................................... 269SGSCATTER .............................................................................................................. 2716.7 Case Study: Using the SG Procedures ................................................................... 2736.8 Enhancing SG Displays—Options with SG Procedure Statements .................. 2796.9 Using Annotate Data Sets to Enhance SG Displays ............................................2846.10 Using Attribute Maps to Enhance SG Displays ................................................... 2876.11 Exercises .....................................................................................................................2907. Processing Text .................................................................................................................... 2937.1 Cleaning and Processing Text Data ....................................................................... 2937.2 Starting with Character Functions ......................................................................... 2937.3 Processing Text .......................................................................................................... 2987.4 Case Study: Sentiment in State of the Union Addresses .....................................3027.5 Case Study: Reading Text from a Web Page .........................................................3097.6 Regular Expressions ................................................................................................. 3157.7 Case Study (Revisited)—Applying Regular Expressions ................................... 3197.8 Exercises ..................................................................................................................... 3218. Programming with Matrices and Vectors ..................................................................... 3238.1 Defining a Matrix and Subscripting ...................................................................... 3238.2 Using Diagonal Matrices and Stacking Matrices ................................................. 3298.3 Using Elementwise Operations, Repeating, and Multiplying Matrices ........... 3328.4 Importing a Data Set into SAS/IML and Exporting Matrices fromSAS/IML to a Data Set .............................................................................................333Creating Matrices from SAS Data Sets and Vice Versa ........................................3338.5 Case Study 1: Monte Carlo Integration to Estimate π ..........................................3368.6 Case Study 2: Bisection Root Finder ...................................................................... 3378.7 Case Study 3: Randomization Test Using Matrices Imported from PROCPLAN ..........................................................................................................................3408.8 Case Study 4: SAS/IML Module to Implement Monte Carlo Integrationto Estimate π ..............................................................................................................3428.9 Storing and Loading SAS/IML Modules ..............................................................3448.10 SAS/IML and R .........................................................................................................3458.11 Exercises .....................................................................................................................350References ...................................................................................................................................355Index ............................................................................................................................................. 357

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Book SynopsisFrom the Reviews[This book] contains an excellent blend of both Shiny-specific topics and practical advice from software development that fits in nicely with Shiny apps. You will find many nuggets of wisdom sprinkled throughout these chapters.Eric Nantz, Host of the R-Podcast and the Shiny Developer Series (from the Foreword)[This] book is a gradual and pleasant invitation to the production-ready shiny apps world. It exposes a comprehensive and robust workflow powered by the {golem} package. [It] fills the not yet covered gap between shiny app development and deployment in such a thrilling way that it may be read in one sitting. In the industry world, where processes robustness is a key toward productivity, this book will indubitably have a tremendous impact.David Granjon, Sr. Expert Data Science, NovartisPresented in full color, Engineering Production-Grade ShinTrade Review"ThinkR’s book is a gradual and pleasant invitation to the production-ready shiny apps world. It focuses on the unfortunately too often forgotten general principles necessary to be successful in this quest, before exposing a comprehensive and robust workflow powered by the {golem} package. This books fills the not yet covered gap between shiny app development and deployment in such a thrilling way that it may be read in one sitting. Readers will appreciate the number of exclusive references like {shinypsum}, {gargoyle}, {crrry} and {dockerfiler} that will definitely help them to reach the production-ready graal. In the industry world, where processes robustness is a key toward productivity, this book will indubitably have a tremendous impact." – David Granjon, Sr. Expert Data Science, Novartis"[This book] contains an excellent blend of both Shiny-specific topics … and practical advice from software development that fits in nicely with Shiny apps. You will find many nuggets of wisdom sprinkled throughout these chapters…."– Eric Nantz, Host of the R-Podcast and the Shiny Developer Series (from the Foreword)Table of Contents1. About Successful Shiny Apps. 2. Planning Ahead. 3. Structuring your Project. 4. Introduction to {golem}. 5. The workflow. 6. UX Matters. 7. Don’t rush into coding. 8. Setting up for success with {golem} (#settingupsuccess). 9. Building an “ipsum-app”. 10. Building the app with {golem} 11. Build yourself a safety net. 12. Version Control. 13. Deploy your application. 14. The Need for Optimization. 15. Common Application Caveats. 16. Optimizing {shiny} Code. 17. Using JavaScript. 18. A Gentle Introduction to CSS. Appendix.

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