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Introduction to Bayesian Estimation and Copula Models of Dependence
Presents an introduction to Bayesian statistics, presents an emphasis on Bayesian methods (prior and posterior), Bayes estimation, prediction, MCMC,Bayesian regression, and Bayesian analysis of statistical modelsof dependence, and features a focus on copulas for risk management Introduction to Bayesian Estimation and Copula Models of Dependence emphasizes the applications of Bayesian analysis to copula modeling and equips readers with the tools needed to implement the procedures of Bayesian estimation in copula models of dependence. This book is structured in two parts: the first four chapters serve as a general introduction to Bayesian statistics with a clear emphasis on parametric estimation and the following four chapters stress statistical models of dependence with a focus of copulas. A review of the main concepts is discussed along with the basics of Bayesian statistics including prior information and experimental data, prior and posterior distributions, with an emphasis on Bayesian parametric estimation. The basic mathematical background of both Markov chains and Monte Carlo integration and simulation is also provided. The authors discuss statistical models of dependence with a focus on copulas and present a brief survey of pre-copula dependence models. The main definitions and notations of copula models are summarized followed by discussions of real-world cases that address particular risk management problems. In addition, this book includes: • Practical examples of copulas in use including within the Basel Accord II documents that regulate the world banking system as well as examples of Bayesian methods within current FDA recommendations • Step-by-step procedures of multivariate data analysis and copula modeling, allowing readers to gain insight for their own applied research and studies • Separate reference lists within each chapter and end-of-the-chapter exercises within Chapters 2 through 8 • A companion website containing appendices: data files and demo files in Microsoft® Office Excel®, basic code in R, and selected exercise solutions Introduction to Bayesian Estimation and Copula Models of Dependence is a reference and resource for statisticians who need to learn formal Bayesian analysis as well as professionals within analytical and risk management departments of banks and insurance companies who are involved in quantitative analysis and forecasting. This book can also be used as a textbook for upper-undergraduate and graduate-level courses in Bayesian statistics and analysis. ARKADY SHEMYAKIN, PhD, is Professor in the Department of Mathematics and Director of the Statistics Program at the University of St. Thomas. A member of the American Statistical Association and the International Society for Bayesian Analysis, Dr. Shemyakin's research interests include informationtheory, Bayesian methods of parametric estimation, and copula models in actuarial mathematics, finance, and engineering. ALEXANDER KNIAZEV, PhD, is Associate Professo
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Thematic List of Descriptors - Sociology
This text is published as a companion to the "International Bibliography of the Social Sciences". First published in 1952, the IBSS is produced annually in four parts - Anthropology, Economics, Political Science, Sociology - and has been widely acclaimed as a tool for librarians, university departments, research institutions, public and private institutions, and indeed for all whose work requires reference to the current literature in any of the fields comprised within its scope. Companion Descriptor lists are now available for all four main subject areas. All volumes of the IBSS published to date have been indexed and from this a language representing every subject covered in this bibliography has been developed. This language can be used for indexing social science publications as well as for retrieving the references stored in the IBSS data bank. The Descriptor List falls into two parts: alphabetical and thematic. In the alphabetic section, terms appear both in English and French. Cross reference is made to all four volumes of the IBSS. The thematic section corresponds to the relevant volume of the four IBSS volumes.
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Turn to Biographical Methods in Social Science
Biographical research methods have become a useful and popular tool for contemporary social scientists. This book combines an exploration of the historical and philosophical origins of this field of qualitative research with comparative examples of the different ways that biographical methods have been successfully applied internationally. Through these many illustrative examples of socio-biography in process, the authors show how formal textual analysis, whilst uncovering hidden emotional defences, can also shed light on wider historical processes of societal transformation. Topics discussed include: individual and linked lives; generational change; political influences on memory and identity; biographical work in reflexive societies; narrativity and empowerment in professional practice; and ways of theorizing and generalising from case studies. This book seeks to promote debate and provide opportunities for students and researchers to widen their uses of narrative research.
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Introduction to Science and Technology Studies
An Introduction to Science and Technology Studies, Second Edition reflects the latest advances in the field while continuing to provide students with a road map to the complex interdisciplinary terrain of science and technology studies. Distinctive in its attention to both the underlying philosophical and sociological aspects of science and technologyExplores core topics such as realism and social construction, discourse and rhetoric, objectivity, and the public understanding of science Includes numerous empirical studies and illustrative examples to elucidate the topics discussedNow includes new material on political economies of scientific and technological knowledge, and democratizing technical decisionsOther features of the new edition include improved readability, updated references, chapter reorganization, and more material on medicine and technology
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Practical Introduction to Econometric Methods
The text is aimed at final-year undergraduate students or those at the graduate level doing econometrics for the first time. It is an introductory course in the theory and practice of classical and modern econometric methods. A proper study of the material will allow the reader to understand the scope and limitations of classical and modern econometric techniques; read, write and properly interpret articles and reports of an applied econometric nature; and be in a position to build upon the elements of econometric theory and practice introduced in the book. Although some basic knowledge of matrix algebra and elementary statistical theory will be assumed, much of it is covered in the text. All the main theoretical concepts are illustrated with the use of econometric software, mainly Eveiws.
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Introduction to the Sociology of Health and Illness
"An accessible and highly readable introduction to the Sociology of Health and Illness through the inclusion of key theorists, concepts, and theories, with reference to contemporary health concerns and recent relevant research." - Kylie Baldwin, De Montfort University"Guides us through the many reasons for the centrality of health, showing clearly that health and illness are the products not just of our biology but of the society into which we are born...an authoritative analysis of the social nature of health." - Ray Fitzpatrick, University of OxfordThis bestselling text introduces students to the core principles of the sociology of health, demonstrating the relationship between social structures and the production and distribution of health and disease in modern society. Written with a truly sociological and critical perspective, the book tackles themes such as class, gender and ethnicity, and engages with a range of theories and theorists, including Foucault, Fleck, Parsons, Weber, and Kuhn.The third edition has been thoroughly updated to include the latest cutting-edge thinking in the area, with new empirical examples, updated references, and new sections on ′Thought Styles after Fleck’, and ‘Transformations of the Medical Profession.′ It also uses helpful learning features including chapter overviews, case studies, summaries and further reading suggestions, to provide stimulating and thought-provoking exercises for students in health, nursing and sociology schools.
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Introduction to Regression Methods for Public Health Using R
Introduction to Regression Methods for Public Health Using R teaches regression methods for continuous, binary, ordinal, and time-to-event outcomes using R as a tool. Regression is a useful tool for understanding the associations between an outcome and a set of explanatory variables, and regression methods are commonly used in many fields, including epidemiology, public health, and clinical research. The focus of this book is on understanding and fitting regression models, diagnosing model fit, and interpreting and writing up results. Examples are drawn from public health and clinical studies. Designed for students, researchers, and practitioners with a basic understanding of introductory statistics, this book teaches the basics of regression and how to implement regression methods using R, allowing the reader to enhance their understanding and begin to grasp new concepts and models.The text includes an overview of regression (Chapter 2); how to examine and summarize the data (Chapter 3), simple (Chapter 4) and multiple (Chapter 5) linear regression; binary, ordinal, and conditional logistic regression, and log-binomial regression (Chapter 6); Cox proportional hazards regression (survival analysis) (Chapter 7); handling data arising from a complex survey design (Chapter 8); and multiple imputation of missing data (Chapter 9). Each chapter closes with a comprehensive set of exercises.Key Features:Comprehensive coverage of the most commonly used regression methods, as well as how to use regression with complex survey data or missing data.Accessible to those with only a first course in statistics.Serves as a course textbook, as well as a reference for public health and clinical researchers seeking to learn regression and/or how to use R to do regression analyses.Includes examples of how to diagnose the fit of a regression model.Includes examples of how to summarize, visualize, table, and write up the results.Includes R code to run the examples.
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Introduction to Computable General Equilibrium Models
Computable general equilibrium (CGE) models play an important role in supporting public-policy making on such issues as trade, climate change and taxation. This significantly revised volume, keeping pace with the next-generation standard CGE model, is the only undergraduate-level introduction of its kind. The volume utilizes a graphical approach to explain the economic theory underlying a CGE model, and provides results from simple, small-scale CGE models to illustrate the links between theory and model outcomes. Its eleven hands-on exercises introduce modelling techniques that are applied to real-world economic problems. Students learn how to integrate their separate fields of economic study into a comprehensive, general equilibrium perspective as they develop their skills as producers or consumers of CGE-based analysis.
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Introduction to Longitudinal Research
One of the major changes in the social science research landscape during the 1990s has been the introduction of computerized panel surveys in Europe and the US which make longitudinal data widely available to graduate students. Elisabetta Ruspini here provides a concise yet comprehensive introduction to the issues involved in this kind of research. The book: defines the concept of longitudinal research; gives guidance on sources of longitudinal data in Europe and the US and their strengths and weaknesses; discusses the choices that need to be made in this kind of research - for instance the advantages and disadvantages of certain types of research data and of different types of analysis; and highlights some of the problems involved, like the issue of comparability within longitudinal research.
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Introduction to Probability
Introduction to Probability, Second Edition, discusses probability theory in a mathematically rigorous, yet accessible way. This one-semester basic probability textbook explains important concepts of probability while providing useful exercises and examples of real world applications for students to consider. This edition demonstrates the applicability of probability to many human activities with examples and illustrations. After introducing fundamental probability concepts, the book proceeds to topics including conditional probability and independence; numerical characteristics of a random variable; special distributions; joint probability density function of two random variables and related quantities; joint moment generating function, covariance and correlation coefficient of two random variables; transformation of random variables; the Weak Law of Large Numbers; the Central Limit Theorem; and statistical inference. Each section provides relevant proofs, followed by exercises and useful hints. Answers to even-numbered exercises are given and detailed answers to all exercises are available to instructors on the book companion site. This book will be of interest to upper level undergraduate students and graduate level students in statistics, mathematics, engineering, computer science, operations research, actuarial science, biological sciences, economics, physics, and some of the social sciences.
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Introduction to Statistical Inference and Its Applications with R
Emphasizing concepts rather than recipes, An Introduction to Statistical Inference and Its Applications with R provides a clear exposition of the methods of statistical inference for students who are comfortable with mathematical notation. Numerous examples, case studies, and exercises are included. R is used to simplify computation, create figures, and draw pseudorandom samples—not to perform entire analyses.After discussing the importance of chance in experimentation, the text develops basic tools of probability. The plug-in principle then provides a transition from populations to samples, motivating a variety of summary statistics and diagnostic techniques. The heart of the text is a careful exposition of point estimation, hypothesis testing, and confidence intervals. The author then explains procedures for 1- and 2-sample location problems, analysis of variance, goodness-of-fit, and correlation and regression. He concludes by discussing the role of simulation in modern statistical inference.Focusing on the assumptions that underlie popular statistical methods, this textbook explains how and why these methods are used to analyze experimental data.
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Guide to Quantitative History
This book introduces statistical techniques for the particular needs of historians. The approach is practical and applied; alternative ways of resolving methodological problems are worked through. The emphasis is on applying the fundamentals of statistical theory to the specific research interests of historians and available historical sources. A special feature of the book is a discussion of the historical evolution of statistical techniques as a means of understanding their current applications and interpretations.
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Introduction to Statistical Physics
This textbook covers the basic principles of statistical physics and thermodynamics. The text is pitched at the level equivalent to first-year graduate studies or advanced undergraduate studies. It presents the subject in a straightforward and lively manner. After reviewing the basic probability theory of classical thermodynamics, the author addresses the standard topics of statistical physics. The text demonstrates their relevance in other scientific fields using clear and explicit examples. Later chapters introduce phase transitions, critical phenomena and non-equilibrium phenomena.
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Introduction to Modeling and Analysis of Stochastic Systems
This book provides a self-contained review of all the relevant topics in probability theory. A software package called MAXIM, which runs on MATLAB, is made available for downloading. Vidyadhar G. Kulkarni is Professor of Operations Research at the University of North Carolina at Chapel Hill.
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Introduction to Survey Research, Polling, and Data Analysis
"The writing style is mature and experienced. There is noticeable care in the presentation of materials and appropriate sensitivity to the intended audience. . . . The book categorizes large bodies of wisdom in the conduct of survey research over the past half century and provides ′′rules-of-thumb′′ guidance to the production, analysis, and consumption of survey data." --Steve Seitz, University of Illinois "The authors have provided a well-written update of their textbook, which will be useful as a textbook or supplement in upper-division undergraduate or introductory graduate courses on survey research, statistical data analysis, or research design. The book provides readers with the knowledge that enables them to evaluate the results of survey research. It sensitizes the readers to potential problems that arise from flaws in research design, sampling, questionnaire construction, data collection, coding, data analysis techniques, and improper presentation or interpretation of findings." --Mike Margolis, University of Cincinnati Why should we believe the results of surveys based on relatively few interviews? What are the appropriate cautions in interpreting survey reports? Aimed at answering these questions, An Introduction to Survey Research, Polling, and Data Analysis, Third Edition describes how surveys are conducted in such a way that they can be believed, explains how to read statistical reports and analyze data, and provides guidelines that are useful in evaluating polls. Using frequent examples from contemporary, large national surveys and polls as well as from the media, the authors stress understanding tables thoroughly before moving to interval statistics. In addition, they cover survey design, sampling and question-writing steps, interviewing and coding strategies, and survey analysis from frequency distributions and cross-tabulations to control tables and correlation/regression. They also cover the ethics of survey research and how to read and write survey research reports. Readers of this book will learn to distinguish good from bad polls and to better design and conduct their own surveys and polls.
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Introduction to the Qualitative Theory of Differential Systems
The book deals with continuous piecewise linear differential systems in the plane with three pieces separated by a pair of parallel straight lines. Moreover, these differential systems are symmetric with respect to the origin of coordinates. This class of systems driven by concrete applications is of interest in engineering, in particular in control theory and the design of electric circuits. By studying these particular differential systems we will introduce the basic tools of the qualitative theory of ordinary differential equations, which allow us to describe the global dynamics of these systems including the infinity. The behavior of their solutions, their parametric stability or instability and their bifurcations are described. The book is very appropriate for a first course in the qualitative theory of differential equations or dynamical systems, mainly for engineers, mathematicians, and physicists.
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Introduction to Scientific Programming
"Introduction to Computational Science" was developed over a period of two years at the University of Utah Department of Computer Science in conjunction with the U.S. Department of Energy-funded Undergraduate Computation in Engineering Science (UCES) program. Each chapter begins by introducing a problem and then guiding the student through its solution. The computational techniques needed to solve the problem are developed as necassary, making the motivation for learning the computing alwasy apparent. Each chapter will introduce a single problem that will be used to motivate a single computing concept. The notes currently consist of 15 chapters. The first seven chapters deal with Maple and the last eight with C. The textbook will contain 20 to 30 chapters covering a similar mix of concepts at a finer level of detail.
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Introduction to Statistical Modelling and Inference
The complexity of large-scale data sets (“Big Data”) has stimulated the development of advanced computational methods for analysing them. There are two different kinds of methods to aid this. The model-based method uses probability models and likelihood and Bayesian theory, while the model-free method does not require a probability model, likelihood or Bayesian theory. These two approaches are based on different philosophical principles of probability theory, espoused by the famous statisticians Ronald Fisher and Jerzy Neyman.Introduction to Statistical Modelling and Inference covers simple experimental and survey designs, and probability models up to and including generalised linear (regression) models and some extensions of these, including finite mixtures. A wide range of examples from different application fields are also discussed and analysed. No special software is used, beyond that needed for maximum likelihood analysis of generalised linear models. Students are expected to have a basic mathematical background in algebra, coordinate geometry and calculus.Features• Probability models are developed from the shape of the sample empirical cumulative distribution function (cdf) or a transformation of it.• Bounds for the value of the population cumulative distribution function are obtained from the Beta distribution at each point of the empirical cdf.• Bayes’s theorem is developed from the properties of the screening test for a rare condition.• The multinomial distribution provides an always-true model for any randomly sampled data.• The model-free bootstrap method for finding the precision of a sample estimate has a model-based parallel – the Bayesian bootstrap – based on the always-true multinomial distribution.• The Bayesian posterior distributions of model parameters can be obtained from the maximum likelihood analysis of the model.This book is aimed at students in a wide range of disciplines including Data Science. The book is based on the model-based theory, used widely by scientists in many fields, and compares it, in less detail, with the model-free theory, popular in computer science, machine learning and official survey analysis. The development of the model-based theory is accelerated by recent developmentsin Bayesian analysis.
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Introduction to Small Area Estimation Techniques
This guide to small area estimation aims to help users compile more reliable granular or disaggregated data in cost-effective ways.It explains small area estimation techniques with examples of how the easily accessible R analytical platform can be used to implement them, particularly to estimate indicators on poverty, employment, and health outcomes. The guide is intended for staff of national statistics offices and for other development practitioners. It aims to help them to develop and implement targeted socioeconomic policies to ensure that the vulnerable segments of societies are not left behind, and to monitor progress toward the Sustainable Development Goals.
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Introduction to Implicit Bias
Written by a diverse range of scholars, this accessible introductory volume asks: What is implicit bias? How does implicit bias compromise our knowledge of others and social reality? How does implicit bias affect us, as individuals and participants in larger social and political institutions, and what can we do to combat biases? An interdisciplinary enterprise, the volume brings together the philosophical perspective of the humanities with the perspective of the social sciences to develop rich lines of inquiry. Its twelve chapters are written in a non-technical style, using relatable examples that help readers understand what implicit bias is, its significance, and the controversies surrounding it. Each chapter includes discussion questions and additional annotated reading suggestions, and a companion webpage contains teaching resources. The volume is an invaluable resource for students—and researchers—seeking to understand criticisms surrounding implicit bias, as well as how one might answer them by adopting a more nuanced understanding of bias and its role in maintaining social injustice.
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Introduction to Population Modeling
The text of this monograph represents the author's lecture notes from a course taught in the Department of Applied Mathematics and Statistics at the State University of New York at Stony Brook in the Spring of 1977. On account of its origin as lecture notes, some sections of the text are telegraphic in style while other portions are overly detailed. This stylistic foible has not been modified as it does not appear to detract seriously from the readability and it does help to indicate which topics were stressed. The audience for the course at Stony Brook was composed almost entirely of fourth year undergraduates majoring in the mathematical sciences. All of these students had studied at least four semesters of calculus and one of probability; few had any prior experience with either differential equations or ecology. It seems prudent to point out that the author's background is in engineering and applied mathematics and not in the biological sciences. It is hoped that this is not painfully obvious. -vii- The focus of the monograph is on the formulation and solution of mathematical models; it makes no pretense of being a text in ecology. The idea of a population is employed mainly as a pedagogic tool, providing unity and intuitive appeal to the varied mathematical ideas introduced. If the biological setting is stripped away, what remains can be interpreted as topics on the qualitative behavior of differential and difference equations.
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Introduction to Statistical Machine Learning
Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks.
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Social Science, Technical Systems, and Cooperative Work
This book is the first to directly address the question of how to bridge what has been termed the "great divide" between the approaches of systems developers and those of social scientists to computer supported cooperative work--a question that has been vigorously debated in the systems development literature. Traditionally, developers have been trained in formal methods and oriented to engineering and formal theoretical problems; many social scientists in the CSCW field come from humanistic traditions in which results are reported in a narrative mode. In spite of their differences in style, the two groups have been cooperating more and more in the last decade, as the "people problems" associated with computing become increasingly evident to everyone. The authors have been encouraged to examine, rigorously and in depth, the theoretical basis of CSCW. With contributions from field leaders in the United Kingdom, France, Scandinavia, Mexico, and the United States, this volume offers an exciting overview of the cutting edge of research and theory. It constitutes a solid foundation for the rapidly coalescing field of social informatics. Divided into three parts, this volume covers social theory, design theory, and the sociotechnical system with respect to CSCW. The first set of chapters looks at ways of rethinking basic social categories with the development of distributed collaborative computing technology--concepts of the group, technology, information, user, and text. The next section concentrates more on the lessons that can be learned at the design stage given that one wants to build a CSCW system incorporating these insights--what kind of work does one need to do and how is understanding of design affected? The final part looks at the integration of social and technical in the operation of working sociotechnical systems. Collectively the contributors make the argument that the social and technical are irremediably linked in practice and so the "great divide" not only should be a thing of the past, it should never have existed in the first place.
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Quantitative and Applied Research Methodology in Economics
This book is an illustrative and comprehensive guide designed to help readers understand and navigate the complex world of academic writing and research in economics.Written by experienced researchers, this book offers theoretical and practical insights into the research process. It provides an understanding of the foundations of the research process like research design, methodology, problem definition, data collection, and analysis, among others. The authors also share insights into the process of preparing, proofreading, and publishing academic papers. With their experience in the field of academic research to this book, they provide practical examples and step-by-step guidance to assist in research-related issues. The section on how to prepare and publish academic papers is a must-read for students and early-career researchers, as it offers valuable guidance on how to succeed in the highly competitive world of academic publishing.With its clear and concise writing, this book will be an indispensable resource for undergraduate and postgraduate economics students, teachers, independent readers, and early-career researchers as well as those seeking a deeper understanding of research methodology in economics.
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Introduction to Statistics
The introductory statistics course presents serious pedagogical problems to the instructor. For the great majority of students, the course represents the only formal contact with statistical thinking that he or she will have in college. Students come from many different fields of study, and a large number suffer from math anxiety. Thus, an instructor who is willing to settle for some limited objectives will have a much better chance of success than an instructor who aims for a broad exposure to statistics. Many statisticians agree that the primary objective of the introductory statistics course is to introduce students to variability and uncertainty and how to cope with them when drawing inferences from observed data. Addi tionally, the introductory COurse should enable students to handle a limited number of useful statistical techniques. The present text, which is the successor to the author's Introduction to Statistics: A Nonparametric Approach (Houghton Mifflin Company, Boston, 1976), tries to meet these objectives by introducing the student to the ba sic ideas of estimation and hypothesis testing early in the course after a rather brief introduction to data organization and some simple ideas about probability. Estimation and hypothesis testing are discussed in terms of the two-sample problem, which is both conceptually simpler and more realistic than the one-sample problem that customarily serves as the basis for the discussion of statistical inference.
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Introduction to Latent Variable Models
Latent variable models are used in many areas of the social and behavioural sciences, and the increasing availability of computer packages for fitting such models is likely to increase their popularity. This book attempts to introduce such models to applied statisticians and research workers interested in exploring the structure of covari ance and correlation matrices in terms of a small number of unob servable constructs. The emphasis is on the practical application of the procedures rather than on detailed discussion of their mathe matical and statistical properties. It is assumed that the reader is familiar with the most commonly used statistical concepts and methods, particularly regression, and also has a fair knowledge of matrix algebra. My thanks are due to my colleagues Dr David Hand and Dr Graham Dunn for helpful comments on the book, to Mrs Bertha Lakey for her careful typing of a difficult manuscript and to Peter Cuttance for assistance with the LlSREL package. In addition the text clearly owes a great deal to the work on structural equation models published by Karl Joreskog, Dag Sorbom, Peter Bentler, Michael Browne and others.
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Textual Introduction to Social and Political Theory
This book offers a stimulating new approach to studying social and political theory. It combines specially selected extracts from the political classics with original and insightful essays offering a commentary upon them. The reader is drawn into a dialogue with the Western political tradition's principal thinkers, whose ideas provide a common currency in which to debate the problems facing modern societies. Each of the twelve chapters combines extracts from two (or in one case three) political philosophers on a key political concept with a commentary essay. Each chapter does more than just introduce the reader to the classics; it also explains, via the commentary essay, the key concepts of political debate, and the historical contexts which led the thinkers to their different understandings of the nature of society.
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Introduction to Human-Computer Interaction
This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations.Aimed at undergraduate students in computer science, design, and engineering programs, and master students in dedicated programs, this is the first comprehensive textbook for students of human-computer interaction. While HCI is primarily a research-driven field, the book focuses not only on scientific principles of interaction, but also on the very concrete goal of designing better computing systems. The book revises and synthesizes topics that have been previously scattered across multiple books and papers, including design, engineering, empirical methods, and technology. Although it covers emerging topics like VR and AI, the book places its emphasis on the more time-enduring principles and methods. The book is open access and comes with associated materials for teachers and students, available on the book's companion website.
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Introduction to NFL Analytics with R
It has become difficult to ignore the analytics movement within the NFL. An increasing number of coaches openly integrate advanced numbers into their game plans, and commentators, throughout broadcasts, regularly use terms such as air yards, CPOE, and EPA on a casual basis. This rapid growth, combined with an increasing accessibility to NFL data, has helped create a burgeoning amateur analytics movement, highlighted by the NFL’s annual Big Data Bowl. Because learning a coding language can be a difficult enough endeavour, Introduction to NFL Analytics with R is purposefully written in a more informal format than readers of similar books may be accustomed to, opting to provide step-by-step instructions in a structured, jargon-free mannerKey Coverage:• Installing R, RStudio, and necessary packages• Working and becoming fluent in the tidyverse• Finding meaning in NFL data with examples from all the functions in the nflverse family of packages• Using NFL data to create eye-catching data visualizations• Building statistical models starting with simple regressions and progressing to advanced machine learning models using tidymodels and eXtreme Gradient BoostingThe book is written for novices of R programming all the way to more experienced coders, as well as audiences with differing expected outcomes. Professors can use Introduction to NFL Analytics with R to provide data science lessons through the lens of the NFL, while students can use it as an educational tool to create robust visualizations and machine learning models for assignments. Journalists, bloggers, and arm-chair quarterbacks alike, will find the book helpful to underpin their arguments by providing hard data and visualizations to back up their claims.
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Introduction to Numerical Methods for Variational Problems
This textbook teaches finite element methods from a computational point of view. It focuses on how to develop flexible computer programs with Python, a programming language in which a combination of symbolic and numerical tools is used to achieve an explicit and practical derivation of finite element algorithms. The finite element library FEniCS is used throughout the book, but the content is provided in sufficient detail to ensure that students with less mathematical background or mixed programming-language experience will equally benefit. All program examples are available on the Internet.
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Introduction to Time Series Modeling with Applications in R
Praise for the first edition:[This book] reflects the extensive experience and significant contributions of the author to non-linear and non-Gaussian modeling. … [It] is a valuable book, especially with its broad and accessible introduction of models in the state-space framework. –Statistics in MedicineWhat distinguishes this book from comparable introductory texts is the use of state-space modeling. Along with this come a number of valuable tools for recursive filtering and smoothing, including the Kalman filter, as well as non-Gaussian and sequential Monte Carlo filters.–MAA ReviewsIntroduction to Time Series Modeling with Applications in R, Second Edition covers numerous stationary and nonstationary time series models and tools for estimating and utilizing them. The goal of this book is to enable readers to build their own models to understand, predict and master time series. The second edition makes it possible for readers to reproduce examples in this book by using the freely available R package TSSS to perform computations for their own real-world time series problems.This book employs the state-space model as a generic tool for time series modeling and presents the Kalman filter, the non-Gaussian filter and the particle filter as convenient tools for recursive estimation for state-space models. Further, it also takes a unified approach based on the entropy maximization principle and employs various methods of parameter estimation and model selection, including the least squares method, the maximum likelihood method, recursive estimation for state-space models and model selection by AIC.Along with the standard stationary time series models, such as the AR and ARMA models, the book also introduces nonstationary time series models such as the locally stationary AR model, the trend model, the seasonal adjustment model, the time-varying coefficient AR model and nonlinear non-Gaussian state-space models.About the Author:Genshiro Kitagawa is a project professor at the University of Tokyo, the former Director-General of the Institute of Statistical Mathematics, and the former President of the Research Organization of Information and Systems.
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Introduction to Structural Equation Modeling Using IBM SPSS Statistics and Amos
This comprehensive Second Edition offers readers a complete guide to carrying out research projects involving structural equation modeling (SEM). Updated to include extensive analysis of AMOS′ graphical interface, a new chapter on latent curve models and detailed explanations of the structural equation modeling process, this second edition is the ideal guide for those new to the field. The book includes: Learning objectives, key concepts and questions for further discussion in each chapter.Helpful diagrams and screenshots to expand on concepts covered in the texts.Real life examples from a variety of disciplines to show how SEM is applied in real research contexts.Exercises for each chapter on an accompanying companion website.A new glossary. Assuming no previous experience of the subject, and a minimum of mathematical knowledge, this is the ideal guide for those new to SEM and an invaluable companion for students taking introductory SEM courses in any discipline. Niels J. Blunch was formerly in the Department of Marketing and Statistics at the University of Aarhus, Denmark
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Introduction to Probability with Mathematica
Updated to conform to Mathematica® 7.0, Introduction to Probability with Mathematica®, Second Edition continues to show students how to easily create simulations from templates and solve problems using Mathematica. It provides a real understanding of probabilistic modeling and the analysis of data and encourages the application of these ideas to practical problems. The accompanyingdownloadable resources offer instructors the option of creating class notes, demonstrations, and projects.New to the Second Edition Expanded section on Markov chains that includes a study of absorbing chainsNew sections on order statistics, transformations of multivariate normal random variables, and Brownian motionMore example data of the normal distribution More attention on conditional expectation, which has become significant in financial mathematicsAdditional problems from Actuarial Exam PNew appendix that gives a basic introduction to Mathematica New examples, exercises, and data sets, particularly on the bivariate normal distributionNew visualization and animation features from Mathematica 7.0Updated Mathematica notebooks on the downloadable resources. After covering topics in discrete probability, the text presents a fairly standard treatment of common discrete distributions. It then transitions to continuous probability and continuous distributions, including normal, bivariate normal, gamma, and chi-square distributions. The author goes on to examine the history of probability, the laws of large numbers, and the central limit theorem. The final chapter explores stochastic processes and applications, ideal for students in operations research and finance.
R 3,855.47
Introduction to Enumeration
Written for students taking a second or third year undergraduate course in mathematics or computer science, this book is the ideal companion to a course in enumeration. Enumeration is a branch of combinatorics where the fundamental subject matter is numerous methods of pattern formation and counting. Introduction to Enumeration provides a comprehensive and practical introduction to this subject giving a clear account of fundamental results and a thorough grounding in the use of powerful techniques and tools.Two major themes run in parallel through the book, generating functions and group theory. The former theme takes enumerative sequences and then uses analytic tools to discover how they are made up. Group theory provides a concise introduction to groups and illustrates how the theory can be used to count the number of symmetries a particular object has. These enrich and extend basic group ideas and techniques.The authors present their material through examples that are carefully chosen to establish key results in a natural setting. The aim is to progressively build fundamental theorems and techniques. This development is interspersed with exercises that consolidate ideas and build confidence. Some exercises are linked to particular sections while others range across a complete chapter. Throughout, there is an attempt to present key enumerative ideas in a graphic way, using diagrams to make them immediately accessible. The development assumes some basic group theory, a familiarity with analytic functions and their power series expansion along with some basic linear algebra.
R 1,278.59
R 4,332.32
