This graduate textbook covers topics in statistical theory essential for graduate students preparing for work on a Ph.D. degree in statistics. This new edition has been revised and updated and in this fourth printing, errors have been ironed out. The first chapter provides a quick overview of concepts and results in measure-theoretic probability theory that are useful in statistics. The second chapter introduces some fundamental concepts in statistical decision theory and inference. Subsequent chapters contain detailed studies on some important topics: unbiased estimation, parametric estimation, nonparametric estimation, hypothesis testing, and confidence sets. A large number of exercises in each chapter provide not only practice problems for students, but also many additional results.
This graduate textbook covers topics in statistical theory essential for graduate students preparing for work on a Ph.D. degree in statistics. The first chapter provides a quick overview of concepts and results in measure-theoretic probability theory that are usefulin statistics. The second chapter introduces some fundamental concepts in statistical decision theory and inference. Chapters 3-7 contain detailed studies on some important topics: unbiased estimation, parametric estimation, nonparametric estimation, hypothesis testing, and confidence sets. A large number of exercises in each chapter provide not only practice problems for students, but also many additional results. In addition to the classical results that are typically covered in a textbook of a similar level, this book introduces some topics in modern statistical theory that have been developed in recent years, such as Markov chain Monte Carlo, quasi-likelihoods, empirical likelihoods, statistical functionals, generalized estimation equations, the jackknife, and the bootstrap. Jun Shao is Professor of Statistics at the University of Wisconsin, Madison. Also available: Jun Shao and Dongsheng Tu, The Jackknife and Bootstrap, Springer- Verlag New York, Inc., 1995, Cloth, 536 pp., 0-387-94515-6.
Mathematical Statistics for Economics and Business, Second Edition, provides a comprehensive introduction to the principles of mathematical statistics which underpin statistical analyses in the fields of economics, business, and econometrics. The selection of topics in this textbook is designed to provide students with a conceptual foundation that will facilitate a substantial understanding of statistical applications in these subjects. This new edition has been updated throughout and now also includes a downloadable Student Answer Manual containing detailed solutions to half of the over 300 end-of-chapter problems. After introducing the concepts of probability, random variables, and probability density functions, the author develops the key concepts of mathematical statistics, most notably: expectation, sampling, asymptotics, and the main families of distributions. The latter half of the book is then devoted to the theories of estimation and hypothesis testing with associated examples and problems that indicate their wide applicability in economics and business. Features of the new edition include: a reorganization of topic flow and presentation to facilitate reading and understanding; inclusion of additional topics of relevance to statistics and econometric applications; a more streamlined and simple-to-understand notation for multiple integration and multiple summation over general sets or vector arguments; updated examples; new end-of-chapter problems; a solution manual for students; a comprehensive answer manual for instructors; and a theorem and definition map. This book has evolved from numerous graduate courses in mathematical statistics and econometrics taught by the author, and will be ideal for students beginning graduate study as well as for advanced undergraduates.
A Primer of Ecological Statistics , Second Edition explains fundamental material in probability theory, experimental design, and parameter estimation for ecologists and environmental scientists. The book emphasizes a general introduction to probability theory and provides a detailed discussion of specific designs and analyses that are typically encountered in ecology and environmental science. Appropriate for use as either a stand-alone or supplementary text for upper-division undergraduate or graduate courses in ecological and environmental statistics, ecology, environmental science, environmental studies, or experimental design, the Primer also serves as a resource for environmental professionals who need to use and interpret statistics daily but have little or no formal training in the subject. The book is divided into four parts. Part I discusses the fundamentals of probability and statistical thinking. It introduces the logic and language of probability (Chapter 1), explains common statistical distributions used in ecology (Chapter 2) and important measures of central tendency and spread (Chapter 3), explains P-values, hypothesis testing, and statistical errors (Chapter 4), and introduces frequentist, Bayesian, and Monte Carlo methods of analysis (Chapter 5). Part II discusses how to successfully design and execute field experiments and sampling studies. Topics include design strategies (Chapter 6), a ´bestiary´ of experimental designs (Chapter 7), and transformations and data management (Chapter 8). Part III discusses specific analyses, and covers the material that is the main core of most statistics texts. Topics include regression (Chapter 9), analysis of variance (Chapter 10), categorical data analysis (Chapter 11), and multivariate analysis (Chapter 12). Part IV-new to this edition-discusses two central topics in estimating important ecological metrics. Topics include quantification of biological diversity (Chapter 13) and estimating occupancy, detection probability, and population sizes from marked and unmarked populations (Chapter 14). The book includes a comprehensive glossary, a mathematical appendix on matrix algebra, and extensively annotated tables and figures. Footnotes introduce advanced and ancillary material: some are purely historical, others cover mathematical/statistical proofs or details, and still others address current topics in the ecological literature. Data files and code used for some of the examples, as well as errata, are available online.
Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections. A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights the methods´ great potential and practical applicability in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science.
The ideal review for your intro to probability course More than 40 million students have trusted Schaum´s Outlines for their expert knowledge and helpful solved problems. Written by renowned experts in their respective fields, Schaum´s Outlines cover everything from math to science, nursing to language. The main feature for all these books is the solved problems. Step-by-step, authors walk readers through coming up with solutions to exercises in their topic of choice. * 387 solved problems * Covers all probability fundamentals * No calculus needed * Supports and supplements the leading probability and statistics textbooks * Appropriate for the following courses: Introduction to Probability and Statistics, Probability, Statistics, Introduction to Statistics * Detailed explanations and practice problems in probability and statistics * Comprehensive review of specialized topics in probability and statistics
Mathematical Statistics:Problems and Detailed Solutions De Gruyter Textbook. 1. Auflage Wiebe R. Pestman, Ivo B. Alberink
Statistically SignificantWeisss Introductory Statistics, Tenth Edition, is the ideal textbook for introductory statistics classes that emphasize statistical reasoning and critical thinking. Comprehensive in its coverage, Weisss meticulous style offers careful, detailed explanations to ease the learning process. With more than 1,000 data sets and over 3,000 exercises, this text takes a data-driven approach that encourages students to apply their knowledge and develop statistical understanding. This text contains parallel presentation of critical-value and p-value approaches to hypothesis testing. This unique design allows the flexibility to concentrate on one approach or the opportunity for greater depth in comparing the two. MyStatLab not included. Students, if MyStatLab is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN and course ID. MyStatLab should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information.MyStatLab is an online homework, tutorial, and assessment product designed to personalize learning and improve results. With a wide range of interactive, engaging, and assignable activities, students are encouraged to actively learn and retain tough course concepts.
A crash course in statistics delves into key statistical methods, namely Chi Square, t-test, ANOVA and descriptive statistics. It equally gives an overview of statistical methods as well as various discussions of the statistical tests relating to various database culled from various sources, like the survey of student spending on textbooks, etc. Also, detailed demonstration of various data analysis in SPSS was considered via statistical test. 1. Language: English. Narrator: Andrea Giordani. Audio sample: http://samples.audible.de/bk/acx0/100742/bk_acx0_100742_sample.mp3. Digital audiobook in aax.
Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.