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Author : Vic Barnett
Publisher : Unknown
Page : 287 pages
File Size : 48,8 Mb
Release : 1979
Category : Electronic
ISBN : OCLC:634465576
Author : Vic Barnett
Publisher : John Wiley & Sons
Page : 418 pages
File Size : 40,9 Mb
Release : 1999-08-03
Category : Mathematics
ISBN : 0471976431
This fully updated and revised third edition, presents a wide ranging, balanced account of the fundamental issues across the full spectrum of inference and decision-making. Much has happened in this field since the second edition was published: for example, Bayesian inferential procedures have not only gained acceptance but are often the preferred methodology. This book will be welcomed by both the student and practising statistician wishing to study at a fairly elementary level, the basic conceptual and interpretative distinctions between the different approaches, how they interrelate, what assumptions they are based on, and the practical implications of such distinctions. As in earlier editions, the material is set in a historical context to more powerfully illustrate the ideas and concepts. Includes fully updated and revised material from the successful second edition Recent changes in emphasis, principle and methodology are carefully explained and evaluated Discusses all recent major developments Particular attention is given to the nature and importance of basic concepts (probability, utility, likelihood etc) Includes extensive references and bibliography Written by a well-known and respected author, the essence of this successful book remains unchanged providing the reader with a thorough explanation of the many approaches to inference and decision making.
Author : Vic Barnett
Publisher : Unknown
Page : 352 pages
File Size : 47,8 Mb
Release : 1982-07-05
Category : Mathematics
ISBN : MINN:31951001032349Z
Provides a general, cross-sectional view of statistical inference and decision-making. Constructs a rational, composite theory for the way individuals react, or should react, stressing interrelationships and conceptual conflicts. Traces the range of different definitions and interpretations of the probability concepts which underlie different approaches to statistical inference and decision-making. Outlines utility theory and its implications for general decision-making. Discusses the Neyman-Pearson approach, Bayesian methods, and Decision Theory. Pays particular attention to the basic concepts of probability, utility, likelihood, sufficiency, conjugacy, and admissibility, both within and between the different approaches.
Author : Francisco J. Samaniego
Publisher : Springer Science & Business Media
Page : 235 pages
File Size : 47,6 Mb
Release : 2010-06-14
Category : Mathematics
ISBN : 9781441959416
The main theme of this monograph is “comparative statistical inference. ” While the topics covered have been carefully selected (they are, for example, restricted to pr- lems of statistical estimation), my aim is to provide ideas and examples which will assist a statistician, or a statistical practitioner, in comparing the performance one can expect from using either Bayesian or classical (aka, frequentist) solutions in - timation problems. Before investing the hours it will take to read this monograph, one might well want to know what sets it apart from other treatises on comparative inference. The two books that are closest to the present work are the well-known tomes by Barnett (1999) and Cox (2006). These books do indeed consider the c- ceptual and methodological differences between Bayesian and frequentist methods. What is largely absent from them, however, are answers to the question: “which - proach should one use in a given problem?” It is this latter issue that this monograph is intended to investigate. There are many books on Bayesian inference, including, for example, the widely used texts by Carlin and Louis (2008) and Gelman, Carlin, Stern and Rubin (2004). These books differ from the present work in that they begin with the premise that a Bayesian treatment is called for and then provide guidance on how a Bayesian an- ysis should be executed. Similarly, there are many books written from a classical perspective.
Author : Murray Aitkin
Publisher : CRC Press
Page : 256 pages
File Size : 53,6 Mb
Release : 2010-06-02
Category : Mathematics
ISBN : 9781420093445
Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new solutions to difficult model comparison problems and offers direct
Author : Deborah G. Mayo
Publisher : Cambridge University Press
Page : 503 pages
File Size : 50,5 Mb
Release : 2018-09-20
Category : Mathematics
ISBN : 9781107054134
Unlock today's statistical controversies and irreproducible results by viewing statistics as probing and controlling errors.
Author : Sarmento, Rui,Costa, Vera
Publisher : IGI Global
Page : 197 pages
File Size : 40,6 Mb
Release : 2017-01-06
Category : Business & Economics
ISBN : 9781522519898
The application of statistics has proliferated in recent years and has become increasingly relevant across numerous fields of study. With the advent of new technologies, its availability has opened into a wider range of users. Comparative Approaches to using R and Python for Statistical Data Analysis is a comprehensive source of emerging research and perspectives on the latest computer software and available languages for the visualization of statistical data. By providing insights on relevant topics, such as inference, factor analysis, and linear regression, this publication is ideally designed for professionals, researchers, academics, graduate students, and practitioners interested in the optimization of statistical data analysis.
Author : Jason Hsu
Publisher : CRC Press
Page : 306 pages
File Size : 49,6 Mb
Release : 1996-02-01
Category : Mathematics
ISBN : 0412982811
Multiple Comparisons introduces simultaneous statistical inference and covers the theory and techniques for all-pairwise comparisons, multiple comparisons with the best, and multiple comparisons with a control. The author describes confidence intervals methods and stepwise exposes abuses and misconceptions, and guides readers to the correct method for each problem. Discussions also include the connections with bioequivalence, drug stability, and toxicity studies Real data sets analyzed by computer software packages illustrate the applications presented.
Author : Paul H. Garthwaite,I. T. Jolliffe,Byron Jones
Publisher : OUP Oxford
Page : 346 pages
File Size : 45,5 Mb
Release : 2002
Category : Mathematics
ISBN : 0198572263
Statistical inference is the foundation on which much of statistical practice is built. The book covers the topic at a level suitable for students and professionals who need to understand these foundations.
Author : D. R. Cox
Publisher : Cambridge University Press
Page : 227 pages
File Size : 53,9 Mb
Release : 2006-08-10
Category : Mathematics
ISBN : 9781139459136
In this definitive book, D. R. Cox gives a comprehensive and balanced appraisal of statistical inference. He develops the key concepts, describing and comparing the main ideas and controversies over foundational issues that have been keenly argued for more than two-hundred years. Continuing a sixty-year career of major contributions to statistical thought, no one is better placed to give this much-needed account of the field. An appendix gives a more personal assessment of the merits of different ideas. The content ranges from the traditional to the contemporary. While specific applications are not treated, the book is strongly motivated by applications across the sciences and associated technologies. The mathematics is kept as elementary as feasible, though previous knowledge of statistics is assumed. The book will be valued by every user or student of statistics who is serious about understanding the uncertainty inherent in conclusions from statistical analyses.
Author : Sharon Roe Anderson,Ariane Auquier,Walter W. Hauck,David Oakes,Walter Vandaele,Herbert I. Weisberg
Publisher : John Wiley & Sons
Page : 309 pages
File Size : 43,6 Mb
Release : 2009-09-25
Category : Mathematics
ISBN : 9780470317204
Brings together techniques for the design and analysis of comparative studies. Methods include multivariate matching, standardization and stratification, analysis of covariance, logit analysis, and log linear analysis. Quantitatively assesses techniques' effectiveness in reducing bias. Discusses hypothesis testing, survival analysis, repeated measure design, and causal inference from comparative studies.
Author : Helio S. Migon,Dani Gamerman,Francisco Louzada
Publisher : CRC Press
Page : 363 pages
File Size : 55,8 Mb
Release : 2014-09-03
Category : Mathematics
ISBN : 9781439878828
A Balanced Treatment of Bayesian and Frequentist Inference- Statistical Inference: An Integrated Approach, Second Edition presents an account of the Bayesian and frequentist approaches to statistical inference. Now with an additional author, this second edition places a more balanced emphasis on both perspectives than the first edition. New to the Second Edition: New material on empirical Bayes and penalized likelihoods and their impact on regression models Expanded material on hypothesis testing, method of moments, bias correction, and hierarchical models More examples and exercises More comparison between the approaches, including their similarities and differences Designed for advanced undergraduate and graduate courses, the text thoroughly covers statistical inference without delving too deep into technical details. It compares the Bayesian and frequentist schools of thought and explores procedures that lie on the border between the two. Many examples illustrate the methods and models, and exercises are included at the end of each chapter.
Author : Charmont Wang
Publisher : CRC Press
Page : 270 pages
File Size : 50,7 Mb
Release : 2020-07-24
Category : Mathematics
ISBN : 9781000148121
This volume focuses on the abuse of statistical inference in scientific and statistical literature, as well as in a variety of other sources, presenting examples of misused statistics to show that many scientists and statisticians are unaware of, or unwilling to challenge the chaotic state of statistical practices.;The book: provides examples of ubiquitous statistical tests taken from the biomedical and behavioural sciences, economics and the statistical literature; discusses conflicting views of randomization, emphasizing certain aspects of induction and epistemology; reveals fallacious practices in statistical causal inference, stressing the misuse of regression models and time-series analysis as instant formulas to draw causal relationships; treats constructive uses of statistics, such as a modern version of Fisher's puzzle, Bayesian analysis, Shewhart control chart, descriptive statistics, chi-square test, nonlinear modeling, spectral estimation and Markov processes in quality control.
Author : Alboukadel Kassambara
Publisher : Unknown
Page : 206 pages
File Size : 48,5 Mb
Release : 2019-11-28
Category : Electronic
ISBN : 1712330888
This R Statistics book provides a solid step-by-step practical guide to statistical inference for comparing groups means using the R software. Additionally, we developed an R package named rstatix, which provides a simple and intuitive pipe-friendly framework, coherent with the `tidyverse` design philosophy, for computing the most common R statistical analyses, including t-test, Wilcoxon test, ANOVA, Kruskal-Wallis and correlation analyses, outliers identification and more. This book is designed to get you doing the statistical tests in R as quick as possible. The book focuses on implementation and understanding of the methods, without having to struggle through pages of mathematical proofs. You will be guided through the steps of summarizing and visualizing the data, checking the assumptions and performing statistical tests in R, interpreting and reporting the results. The main parts of the book include: PART I. Statistical tests and assumptions for the comparison of groups means; PART II. comparing two means (t-test, Wilcoxon test, Sign test); PART III. comparing multiple means (ANOVA - Analysis of Variance for independent measures, repeated measures ANOVA, mixed ANOVA, ANCOVA and MANOVA, Kruskal-Wallis test and Friedman test).
Author : Seymour Geisser,Wesley O. Johnson
Publisher : John Wiley & Sons
Page : 218 pages
File Size : 52,5 Mb
Release : 2006-01-27
Category : Mathematics
ISBN : 9780471743125
A fascinating investigation into the foundations of statistical inference This publication examines the distinct philosophical foundations of different statistical modes of parametric inference. Unlike many other texts that focus on methodology and applications, this book focuses on a rather unique combination of theoretical and foundational aspects that underlie the field of statistical inference. Readers gain a deeper understanding of the evolution and underlying logic of each mode as well as each mode's strengths and weaknesses. The book begins with fascinating highlights from the history of statistical inference. Readers are given historical examples of statistical reasoning used to address practical problems that arose throughout the centuries. Next, the book goes on to scrutinize four major modes of statistical inference: * Frequentist * Likelihood * Fiducial * Bayesian The author provides readers with specific examples and counterexamples of situations and datasets where the modes yield both similar and dissimilar results, including a violation of the likelihood principle in which Bayesian and likelihood methods differ from frequentist methods. Each example is followed by a detailed discussion of why the results may have varied from one mode to another, helping the reader to gain a greater understanding of each mode and how it works. Moreover, the author provides considerable mathematical detail on certain points to highlight key aspects of theoretical development. The author's writing style and use of examples make the text clear and engaging. This book is fundamental reading for graduate-level students in statistics as well as anyone with an interest in the foundations of statistics and the principles underlying statistical inference, including students in mathematics and the philosophy of science. Readers with a background in theoretical statistics will find the text both accessible and absorbing.
