Quantification methodology of categorical data is a popular topic in many branches of science. Most books, however, are either too advanced for those who need it, or too elementary to gain insight into its potential. This book fills the gap between these extremes, and provides specialists with an easy and comprehensive reference, and others with a complete treatment of dual scaling methodology -- starting with motivating examples, followed by an introductory discussion of necessary quantitative skills, and ending with different perpsectives on dual scaling with examples, advanced topics, and future possibilities.
This book attempts to successively upgrade readers' readiness for handling analysis of qualitative, categorical, and non-metric data, without overloading them. The writing style is very friendly, and difficult topics are always accompanied by simple illlustrative examples.
There are a number of topics on dual scaling which were previously addressed only in journal articles or in publications that are not readily available. Integration of these topics into the standard framework makes the current book unique, and its extensive coverage of relevant topics is unprecedented. This book will serve as both reference and textbook for all those who want to analyze categorical data effectively.

Practical in its approach, Applied Bayesian Forecasting and Time Series Analysis provides the theories, methods, and tools necessary for forecasting and the analysis of time series. The authors unify the concepts, model forms, and modeling requirements within the framework of the dynamic linear mode (DLM). They include a complete theoretical development of the DLM and illustrate each step with analysis of time series data. Using real data sets the authors: Explore diverse aspects of time series, including how to identify, structure, explain observed behavior, model structures and behaviors, and interpret analyses to make informed forecasts Illustrate concepts such as component decomposition, fundamental model forms including trends and cycles, and practical modeling requirements for routine change and unusual events Conduct all analyses in the BATS computer programs, furnishing online that program and the more than 50 data sets used in the text The result is a clear presentation of the Bayesian paradigm: quantified subjective judgements derived from selected models applied to time series observations. Accessible to undergraduates, this unique volume also offers complete guidelines valuable to researchers, practitioners, and advanced students in statistics, operations research, and engineering.

Volume I of this two-volume text and reference work begins by providing a foundation in measure and integration theory. It then offers a systematic introduction to probability theory, and in particular, those parts that are used in statistics. This volume discusses the law of large numbers for independent and non-independent random variables, transforms, special distributions, convergence in law, the central limit theorem for normal and infinitely divisible laws, conditional expectations and martingales. Unusual topics include the uniqueness and convergence theorem for general transforms with characteristic functions, Laplace transforms, moment transforms and generating functions as special examples. The text contains substantive applications, e.g., epidemic models, the ballot problem, stock market models and water reservoir models, and discussion of the historical background. The exercise sets contain a variety of problems ranging from simple exercises to extensions of the theory.

Volume II of this two-volume text and reference work concentrates on the applications of probability theory to statistics, e.g., the art of calculating densities of complicated transformations of random vectors, exponential models, consistency of maximum estimators, and asymptotic normality of maximum estimators. It also discusses topics of a pure probabilistic nature, such as stochastic processes, regular conditional probabilities, strong Markov chains, random walks, and optimal stopping strategies in random games. Unusual topics include the transformation theory of densities using Hausdorff measures, the consistency theory using the upper definition function, and the asymptotic normality of maximum estimators using twice stochastic differentiability. With an emphasis on applications to statistics, this is a continuation of the first volume, though it may be used independently of that book. Assuming a knowledge of linear algebra and analysis, as well as a course in modern probability, Volume II looks at statistics from a probabilistic point of view, touching only slightly on the practical computation aspects.

The use of graphical models in applied statistics has increased considerably over recent years and the theory has been greatly developed and extended. This book provides a self-contained introduction to the learning of graphical models from data, and includes detailed coverage of possibilistic networks - a tool that allows the user to infer results from problems with imprecise data.

One of the most important applications of graphical modelling today is data mining - the data-driven discovery and modelling of hidden patterns in large data sets. The techniques described have a wide range of industrial applications, and a quality testing programme at a major car manufacturer is included as a real-life example.

• Provides a self-contained introduction to learning relational, probabilistic and possibilistic networks from data.

• Each concept is carefully explained and illustrated by examples.

• Contains all necessary background material, including modelling under uncertainty, decomposition of distributions, and graphical representation of decompositions.

• Features applications of learning graphical models from data, and problems for further research.

• Includes a comprehensive bibliography.

Graphical Models: Methods for Data Analysis and Mining will be invaluable to researchers and practitioners who use graphical models in their work. Graduate students of applied statistics, computer science and engineering will find this book provides an excellent introduction to the subject.

Using real-life examples from the banking and insurance industries, Quantitative Operational Risk Models details how internal data can be improved based on external information of various kinds. Using a simple and intuitive methodology based on classical transformation methods, the book includes real-life examples of the combination of internal data and external information. A guideline for practitioners, the book begins with the basics of managing operational risk data to more sophisticated and recent tools needed to quantify the capital requirements imposed by operational risk. The book then covers statistical theory prerequisites, and explains how to implement the new density estimation methods for analyzing the loss distribution in operational risk for banks and insurance companies. In addition, it provides: Simple, intuitive, and general methods to improve on internal operational risk assessment Univariate event loss severity distributions analyzed using semiparametric models Methods for the introduction of underreporting information A practical method to combine internal and external operational risk data, including guided examples in SAS and R Measuring operational risk requires the knowledge of the quantitative tools and the comprehension of insurance activities in a very broad sense, both technical and commercial. Presenting a nonparametric approach to modeling operational risk data, Quantitative Operational Risk Models offers a practical perspective that combines statistical analysis and management orientations.

Statistical distributions are important tools to model the characteristics of data sets such as right or left skewness, bi-modality or multi-modality observed in different applied sciences such as engineering, medicine, and finance, among others. The well-known distributions such as normal, Weibull, gamma, Lindley are extensively used because of their simple forms and identifiability properties. However, mostly in the last decade or so, researchers have focused on the more complex and flexible distributions, referred to as Generalized or simply G families of distributions to increase the modeling ability of these distributions by adding one or more shape parameters. The main aim of this edited book is to present new development currently made by various researchers in the field of G families of contributions distributions. The book will help future and current researchers in the field of this research. Some of the objectives are listed below: Develop new univariate continuous and discrete G families of probability distributions. Develop new bivariate continuous and discrete G families of probability distributions. Derive useful mathematical properties such as, ordinary and incomplete moments, moments generating functions, residual life and reversed residual life functions, order statistics, quantile spread ordering and entropies, among others and some bivariate and multivariate extensions of the new and existing models using a simple type copula such as: Farlie Gumbel Morgenstern copula. Modified Farlie Gumbel Morgenstern copula. Clayton copula. Renyi entropy copula. Ali-Mikhail-Haq copula. haracterize the models via several techniques such as: the conditional expectation. the truncated moment. the hazard functions. Mills ratio. certain functions of the random variable. the 1st order statistic. the conditional expectation of the record values. Assess the performance of the used estimation methods via Monte-Carlo simulation studies. Show the wide importance and the flexibility of the new models against the competitive models. Construct some new regression models based on the new proposed G families and use in statistical prediction. Application of many new useful goodness-of-fit tests for right censored validation such as the Nikulin-Rao-Robson goodness-of-fit test, modified Nikulin-Rao-Robson goodness-of-fit test, Bagdonavicius-Nikulin goodness-of-fit test and modified Bagdonavicius-Nikulin goodness-of-fit test to the new families.

Drug development is an iterative process. The recent publications of regulatory guidelines further entail a lifecycle approach. Blending data from disparate sources, the Bayesian approach provides a flexible framework for drug development. Despite its advantages, the uptake of Bayesian methodologies is lagging behind in the field of pharmaceutical development. Written specifically for pharmaceutical practitioners, Bayesian Analysis with R for Drug Development: Concepts, Algorithms, and Case Studies, describes a wide range of Bayesian applications to problems throughout pre-clinical, clinical, and Chemistry, Manufacturing, and Control (CMC) development. Authored by two seasoned statisticians in the pharmaceutical industry, the book provides detailed Bayesian solutions to a broad array of pharmaceutical problems. Features Provides a single source of information on Bayesian statistics for drug development Covers a wide spectrum of pre-clinical, clinical, and CMC topics Demonstrates proper Bayesian applications using real-life examples Includes easy-to-follow R code with Bayesian Markov Chain Monte Carlo performed in both JAGS and Stan Bayesian software platforms Offers sufficient background for each problem and detailed description of solutions suitable for practitioners with limited Bayesian knowledge Harry Yang, Ph.D., is Senior Director and Head of Statistical Sciences at AstraZeneca. He has 24 years of experience across all aspects of drug research and development and extensive global regulatory experiences. He has published 6 statistical books, 15 book chapters, and over 90 peer-reviewed papers on diverse scientific and statistical subjects, including 15 joint statistical works with Dr. Novick. He is a frequent invited speaker at national and international conferences. He also developed statistical courses and conducted training at the FDA and USP as well as Peking University. Steven Novick, Ph.D., is Director of Statistical Sciences at AstraZeneca. He has extensively contributed statistical methods to the biopharmaceutical literature. Novick is a skilled Bayesian computer programmer and is frequently invited to speak at conferences, having developed and taught courses in several areas, including drug-combination analysis and Bayesian methods in clinical areas. Novick served on IPAC-RS and has chaired several national statistical conferences.

Written to reveal statistical deceptions often thrust upon unsuspecting journalists, this book views the use of numbers from a public perspective. Illustrating how the statistical naivete of journalists often nourishes quantitative misinformation, the author's intent is to make journalists more critical appraisers of numerical data so that in reporting them they do not deceive the public. The book frequently uses actual reported examples of misused statistical data reported by mass media and describes how journalists can avoid being taken in by them. Because reports of survey findings seldom give sufficient detail of methods on the actual questions asked, this book elaborates on questions reporters should ask about methodology and how to detect biased questions before reporting the findings to the public. As such, it may be looked upon as an elements of style for reporting statistics.

Summarizes information scattered in the technical literature on a subject too new to be included in most textbooks, but which is of interest to statisticians, and those who use statistics in science and education, at an advanced undergraduate or higher level. Overviews recent research on constructin

The proceedings of this conference contain keynote addresses on recent developments in geotechnical reliability and limit state design in geotechnics. It also contains invited lectures on such topics as modelling of soil variability, simulation of random fields and probability of rock joints.

Contents: Keynote addresses on recent development on geotechnical reliability and limit state design in geotechnics, and invited lectures on modelling of soil variability, simulation of random field, probabilistic of rock joints, and probabilistic design of foundations and slopes. Other papers on analytical techniques in geotechnical reliability, modelling of soil properties, and probabilistic analysis of slopes, embankments and foundations.

This book develops an original theory of decision-making based on the concept of plausibility. The author advocates plausible reasoning as a general philosophical method and demonstrates how it can be applied to problems in argumentation theory, scientific theory choice, risk management, ethics, law, economics, and epistemology. Human decisions are conditioned by formidable uncertainty. The standard resource for dealing rationally with uncertainty is the mathematical concept of probability. The probability calculus is well-known, but since the numerical demands for applying it cannot usually be met, it is not widely applicable. By contrast, the concept of plausibility is widely applicable, but it is little known. This book relies on a generalized concept of plausibility whose strength is its adaptability. The adaptability is due to a novel form of decision theory that takes plausibilities as inputs. This form of decision theory remains applicable to decisions informed by sharp probabilities and utilities, but it can also be applied to decisions that must be made without them. It can aid in the rationally critical enterprise of discriminating good arguments from bad, and this can foster philosophical progress. A Plea for Plausibility will be of interest to scholars and advanced students working in argumentation theory, philosophy of science, ethics, epistemology, economics, law, and risk management.

This book deals with the development of methodology for the analysis of truncated and censored sample data. It is primarily intended as a handbook for practitioners who need simple and efficient methods for the analysis of incomplete sample data.

Maintaining the excellent features that made the first edition so popular, this outstanding reference/text presents the only comprehensive treatment of the theory of point processes and statistical inference for point processes-highlighting both pointprocesses on the real line and sp;,.tial point processes. Thoroughly updated and revised to reflect changes since publication of the firstedition, the expanded Second EdiLion now contains a better organized and easierto-understand treatment of stationary point processes ... expanded treatment ofthe multiplicative intensity model ... expanded treatment of survival analysis . ..broadened consideration of applications ... an expanded and extended bibliographywith over 1,000 references ... and more than 3('() end-of-chapter exercises.

The ingratiating title notwithstanding, this is in no standard sense a text but a monograph, based largely upon the authors' research over a period of years, and intended to be read by sophisticated students of theoretical statistics. No exercises attach to the nine chapters, nor are they interrup

This book walks you through learning probability and statistics from a Bayesian point of view. From an introduction to probability theory through to frameworks for doing rigorous calculations of probability, it discusses Bayes' Theorem before illustrating how to use it in a variety of different situations with data addressing social and psychological issues. The book also: Equips you with coding skills in the statistical modelling language Stan and programming language R. Discusses how Bayesian approaches to statistics compare to classical approaches. Introduces Markov Chain Monte Carlo methods for doing Bayesian statistics through computer simulations, so you understand how Bayesian solutions are implemented. Features include an introduction to each chapter and a chapter summary to help you check your learning. All the examples and data used in the book are also available in the online resources so you can practice at your own pace. For readers with some understanding of basic mathematical functions and notation, this book will get you up and running so you can do Bayesian statistics with confidence.

In response to the growing emphasis on precision in the summarization and integration of research literature, "Advanced BASIC Meta-Analysis" presents an overview of strategies, techniques, and procedures used in meta-analysis.
The book and software provide an integrated and comprehensive combination of meta-analytic tools for the statistical integration of independent study results. "Advanced BASIC Meta-Analysis" has three distinct goals:
* to provide a clear and user-friendly introduction to the procedures and rules of effective meta-analytic integration;
* to present the implicit assumptions and strategies that guide successful meta-analytic integrations; and
* to develop a meta-analytic database management system that allows users to create, modify, and update a database, including the relevant statistical information and predictors, for a given research domain.
The companion software system allows users to perform a full complement of meta-analytic statistical functions with the speed and flexibility of a database management system. It can also construct a wide array of meta-analytic graphic displays. This text and software package serves as a useful introduction to the quantitative assessment of research domains for those new to meta-analyses. It is also a valuable sourcebook for those who have already conducted meta-analyses.

* Targests readers with a background in programming, interested in an introduction/refresher in statistical hypothesis testing * Uses Python throughout * Provides the reader with the opportunity of using the book whenever needed rather than following a sequential path.

In order to make informed decisions, there are three important elements: intuition, trust, and analytics. Intuition is based on experiential learning and recent research has shown that those who rely on their "gut feelings" may do better than those who don't. Analytics, however, are important in a data-driven environment to also inform decision making. The third element, trust, is critical for knowledge sharing to take place. These three elements-intuition, analytics, and trust-make a perfect combination for decision making. This book gathers leading researchers who explore the role of these three elements in the process of decision-making.

The growth of machines and users of the Internet has led to the proliferation of all sorts of data concerning individuals, institutions, companies, governments, universities, and all kinds of known objects and events happening everywhere in daily life. Scientific knowledge is not an exception to the data boom. The phenomenon of data growth in science pushes forth as the number of scientific papers published doubles every 9-15 years, and the need for methods and tools to understand what is reported in scientific literature becomes evident. As the number of academicians and innovators swells, so do the number of publications of all types, yielding outlets of documents and depots of authors and institutions that need to be found in Bibliometric databases. These databases are dug into and treated to hand over metrics of research performance by means of Scientometrics that analyze the toil of individuals, institutions, journals, countries, and even regions of the world. The objective of this book is to assist students, professors, university managers, government, industry, and stakeholders in general, understand which are the main Bibliometric databases, what are the key research indicators, and who are the main players in university rankings and the methodologies and approaches that they employ in producing ranking tables. The book is divided into two sections. The first looks at Scientometric databases, including Scopus and Google Scholar as well as institutional repositories. The second section examines the application of Scientometrics to world-class universities and the role that Scientometrics can play in competition among them. It looks at university rankings and the methodologies used to create these rankings. Individual chapters examine specific rankings that include: QS World University Scimago Institutions Webometrics U-Multirank U.S. News & World Report The book concludes with a discussion of university performance in the age of research analytics.

This book details most common statistical process control tools with many examples for high-volume production. It aims to make elements of high-volume production process control simple and easy to understand. It lets you thoroughly understand process controls instead of blindly trusting software tools that operate as black boxes. If you are dealing with high-volume production as an operator, line supervisor, inspector, process engineer, quality engineer, manufacturing manager, plant manager, or president of the company, you have to understand the statistical process control basics explained in this book in order to be successful.

The first edition of this book (1970) set out a systematic basis for the analysis of binary data and in particular for the study of how the probability of 'success' depends on explanatory variables. The first edition has been widely used and the general level and style have been preserved in the second edition, which contains a substantial amount of new material. This amplifies matters dealt with only cryptically in the first edition and includes many more recent developments. In addition the whole material has been reorganized, in particular to put more emphasis on m.aximum likelihood methods.

There are nearly 60 further results and exercises. The main points are illustrated by practical examples, many of them not in the first edition, and some general essential background material is set out in new Appendices.

Risk Measures and Insurance Solvency Benchmarks: Fixed-Probability Levels in Renewal Risk Models is written for academics and practitioners who are concerned about potential weaknesses of the Solvency II regulatory system. It is also intended for readers who are interested in pure and applied probability, have a taste for classical and asymptotic analysis, and are motivated to delve into rather intensive calculations. The formal prerequisite for this book is a good background in analysis. The desired prerequisite is some degree of probability training, but someone with knowledge of the classical real-variable theory, including asymptotic methods, will also find this book interesting. For those who find the proofs too complicated, it may be reassuring that most results in this book are formulated in rather elementary terms. This book can also be used as reading material for basic courses in risk measures, insurance mathematics, and applied probability. The material of this book was partly used by the author for his courses in several universities in Moscow, Copenhagen University, and in the University of Montreal. Features Requires only minimal mathematical prerequisites in analysis and probability Suitable for researchers and postgraduate students in related fields Could be used as a supplement to courses in risk measures, insurance mathematics and applied probability.

This monograph provides a careful review of the major statistical techniques used to analyze regression data with nonconstant variability and skewness. The authors have developed statistical techniques--such as formal fitting methods and less formal graphical techniques-- that can be applied to many problems across a range of disciplines, including pharmacokinetics, econometrics, biochemical assays, and fisheries research.

While the main focus of the book in on data transformation and weighting, it also draws upon ideas from diverse fields such as influence diagnostics, robustness, bootstrapping, nonparametric data smoothing, quasi-likelihood methods, errors-in-variables, and random coefficients. The authors discuss the computation of estimates and give numerous examples using real data. The book also includes an extensive treatment of estimating variance functions in regression.

Many experiments have shown the human brain generally has very serious problems dealing with probability and chance. A greater understanding of probability can help develop the intuition necessary to approach risk with the ability to make more informed (and better) decisions. The first four chapters offer the standard content for an introductory probability course, albeit presented in a much different way and order. The chapters afterward include some discussion of different games, different "ideas" that relate to the law of large numbers, and many more mathematical topics not typically seen in such a book. The use of games is meant to make the book (and course) feel like fun! Since many of the early games discussed are casino games, the study of those games, along with an understanding of the material in later chapters, should remind you that gambling is a bad idea; you should think of placing bets in a casino as paying for entertainment. Winning can, obviously, be a fun reward, but should not ever be expected. Changes for the Second Edition: New chapter on Game Theory New chapter on Sports Mathematics The chapter on Blackjack, which was Chapter 4 in the first edition, appears later in the book. Reorganization has been done to improve the flow of topics and learning. New sections on Arkham Horror, Uno, and Scrabble have been added. Even more exercises were added! The goal for this textbook is to complement the inquiry-based learning movement. In my mind, concepts and ideas will stick with the reader more when they are motivated in an interesting way. Here, we use questions about various games (not just casino games) to motivate the mathematics, and I would say that the writing emphasizes a "just-in-time" mathematics approach. Topics are presented mathematically as questions about the games themselves are posed. Table of Contents Preface 1. Mathematics and Probability 2. Roulette and Craps: Expected Value 3. Counting: Poker Hands 4. More Dice: Counting and Combinations, and Statistics 5. Game Theory: Poker Bluffing and Other Games 6. Probability/Stochastic Matrices: Board Game Movement 7. Sports Mathematics: Probability Meets Athletics 8. Blackjack: Previous Methods Revisited 9. A Mix of Other Games 10. Betting Systems: Can You Beat the System? 11. Potpourri: Assorted Adventures in Probability Appendices Tables Answers and Selected Solutions Bibliography Biography Dr. David G. Taylor is a professor of mathematics and an associate dean for academic affairs at Roanoke College in southwest Virginia. He attended Lebanon Valley College for his B.S. in computer science and mathematics and went to the University of Virginia for his Ph.D. While his graduate school focus was on studying infinite dimensional Lie algebras, he started studying the mathematics of various games in order to have a more undergraduate-friendly research agenda. Work done with two Roanoke College students, Heather Cook and Jonathan Marino, appears in this book! Currently he owns over 100 different board games and enjoys using probability in his decision-making while playing most of those games. In his spare time, he enjoys reading, cooking, coding, playing his board games, and spending time with his six-year-old dog Lilly.