Measuring Statistical Evidence Using Relative Belief

Author: Michael Evans
Publisher: CRC Press
ISBN: 148224280X
Size: 56.71 MB
Format: PDF, Mobi
View: 7225
Download
A Sound Basis for the Theory of Statistical Inference Measuring Statistical Evidence Using Relative Belief provides an overview of recent work on developing a theory of statistical inference based on measuring statistical evidence. It shows that being explicit about how to measure statistical evidence allows you to answer the basic question of when a statistical analysis is correct. The book attempts to establish a gold standard for how a statistical analysis should proceed. It first introduces basic features of the overall approach, such as the roles of subjectivity, objectivity, infinity, and utility in statistical analyses. It next discusses the meaning of probability and the various positions taken on probability. The author then focuses on the definition of statistical evidence and how it should be measured. He presents a method for measuring statistical evidence and develops a theory of inference based on this method. He also discusses how statisticians should choose the ingredients for a statistical problem and how these choices are to be checked for their relevance in an application.

Statistical Evidence

Author: Richard Royall
Publisher: Routledge
ISBN: 1351414550
Size: 18.30 MB
Format: PDF, ePub, Docs
View: 3257
Download
Interpreting statistical data as evidence, Statistical Evidence: A Likelihood Paradigm focuses on the law of likelihood, fundamental to solving many of the problems associated with interpreting data in this way. Statistics has long neglected this principle, resulting in a seriously defective methodology. This book redresses the balance, explaining why science has clung to a defective methodology despite its well-known defects. After examining the strengths and weaknesses of the work of Neyman and Pearson and the Fisher paradigm, the author proposes an alternative paradigm which provides, in the law of likelihood, the explicit concept of evidence missing from the other paradigms. At the same time, this new paradigm retains the elements of objective measurement and control of the frequency of misleading results, features which made the old paradigms so important to science. The likelihood paradigm leads to statistical methods that have a compelling rationale and an elegant simplicity, no longer forcing the reader to choose between frequentist and Bayesian statistics.

Design And Analysis Of Cross Over Trials Third Edition

Author: Byron Jones
Publisher: CRC Press
ISBN: 1439861420
Size: 62.86 MB
Format: PDF
View: 1079
Download
Design and Analysis of Cross-Over Trials is concerned with a specific kind of comparative trial known as the cross-over trial, in which subjects receive different sequences of treatments. Such trials are widely used in clinical and medical research, and in other diverse areas such as veterinary science, psychology, sports science, and agriculture. The first edition of this book was the first to be wholly devoted to the subject. The second edition was revised to mirror growth and development in areas where the design remained in widespread use and new areas where it had grown in importance. This new Third Edition: Contains seven new chapters written in the form of short case studies that address re-estimating sample size when testing for average bioequivalence, fitting a nonlinear dose response function, estimating a dose to take forward from phase two to phase three, establishing proof of concept, and recalculating the sample size using conditional power Employs the R package Crossover, specially created to accompany the book and provide a graphical user interface for locating designs in a large catalog and for searching for new designs Includes updates regarding the use of period baselines and the analysis of data from very small trials Reflects the availability of new procedures in SAS, particularly proc glimmix Presents the SAS procedure proc mcmc as an alternative to WinBUGS for Bayesian analysis Complete with real data and downloadable SAS code, Design and Analysis of Cross-Over Trials, Third Edition provides a practical understanding of the latest methods along with the necessary tools for implementation.

Statistical Consulting

Author: Javier Cabrera
Publisher: Springer Science & Business Media
ISBN: 1475736630
Size: 10.99 MB
Format: PDF, Docs
View: 2913
Download
This book is intended for the statistician or student interested in becoming a statistical consultant, as well as clients who need to understand what is involved in the consulting process. It discusses different consulting environments, provides detailed descriptions of communication skills a consultant must possess, and provides concrete examples and case-studies of varying complexity. Emphasis is placed on the importance of engaging the client's understanding of the purpose and interpretation of statistical procedures.

Dependence Modeling With Copulas

Author: Harry Joe
Publisher: CRC Press
ISBN: 1466583231
Size: 76.12 MB
Format: PDF, ePub, Docs
View: 1971
Download
Dependence Modeling with Copulas covers the substantial advances that have taken place in the field during the last 15 years, including vine copula modeling of high-dimensional data. Vine copula models are constructed from a sequence of bivariate copulas. The book develops generalizations of vine copula models, including common and structured factor models that extend from the Gaussian assumption to copulas. It also discusses other multivariate constructions and parametric copula families that have different tail properties and presents extensive material on dependence and tail properties to assist in copula model selection. The author shows how numerical methods and algorithms for inference and simulation are important in high-dimensional copula applications. He presents the algorithms as pseudocode, illustrating their implementation for high-dimensional copula models. He also incorporates results to determine dependence and tail properties of multivariate distributions for future constructions of copula models.

Introduction To Credit Risk Modeling Second Edition

Author: Christian Bluhm
Publisher: CRC Press
ISBN: 1584889934
Size: 48.95 MB
Format: PDF, ePub, Mobi
View: 4161
Download
Contains Nearly 100 Pages of New Material The recent financial crisis has shown that credit risk in particular and finance in general remain important fields for the application of mathematical concepts to real-life situations. While continuing to focus on common mathematical approaches to model credit portfolios, Introduction to Credit Risk Modeling, Second Edition presents updates on model developments that have occurred since the publication of the best-selling first edition. New to the Second Edition An expanded section on techniques for the generation of loss distributions Introductory sections on new topics, such as spectral risk measures, an axiomatic approach to capital allocation, and nonhomogeneous Markov chains Updated sections on the probability of default, exposure-at-default, loss-given-default, and regulatory capital A new section on multi-period models Recent developments in structured credit The financial crisis illustrated the importance of effectively communicating model outcomes and ensuring that the variation in results is clearly understood by decision makers. The crisis also showed that more modeling and more analysis are superior to only one model. This accessible, self-contained book recommends using a variety of models to shed light on different aspects of the true nature of a credit risk problem, thereby allowing the problem to be viewed from different angles.

Introduction To High Dimensional Statistics

Author: Christophe Giraud
Publisher: CRC Press
ISBN: 1482237954
Size: 15.15 MB
Format: PDF, ePub, Docs
View: 3463
Download
Ever-greater computing technologies have given rise to an exponentially growing volume of data. Today massive data sets (with potentially thousands of variables) play an important role in almost every branch of modern human activity, including networks, finance, and genetics. However, analyzing such data has presented a challenge for statisticians and data analysts and has required the development of new statistical methods capable of separating the signal from the noise. Introduction to High-Dimensional Statistics is a concise guide to state-of-the-art models, techniques, and approaches for handling high-dimensional data. The book is intended to expose the reader to the key concepts and ideas in the most simple settings possible while avoiding unnecessary technicalities. Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this highly accessible text: Describes the challenges related to the analysis of high-dimensional data Covers cutting-edge statistical methods including model selection, sparsity and the lasso, aggregation, and learning theory Provides detailed exercises at the end of every chapter with collaborative solutions on a wikisite Illustrates concepts with simple but clear practical examples Introduction to High-Dimensional Statistics is suitable for graduate students and researchers interested in discovering modern statistics for massive data. It can be used as a graduate text or for self-study.

Asymptotic Analysis Of Mixed Effects Models

Author: Jiming Jiang
Publisher: CRC Press
ISBN: 1351645595
Size: 77.46 MB
Format: PDF
View: 1254
Download
Large sample techniques are fundamental to all fields of statistics. Mixed effects models, including linear mixed models, generalized linear mixed models, non-linear mixed effects models, and non-parametric mixed effects models are complex models, yet, these models are extensively used in practice. This monograph provides a comprehensive account of asymptotic analysis of mixed effects models. The monograph is suitable for researchers and graduate students who wish to learn about asymptotic tools and research problems in mixed effects models. It may also be used as a reference book for a graduate-level course on mixed effects models, or asymptotic analysis.

Programmer S Guide To Drupal

Author: Jennifer Hodgdon
Publisher: "O'Reilly Media, Inc."
ISBN: 1491912626
Size: 38.52 MB
Format: PDF, Docs
View: 4208
Download
If you’re a web programmer, your experiences have taught you certain lessons—and only some of them apply well to Drupal. Drupal has its own set of programming principles that require a different approach, and many programmers make mistakes when relying on skills they’ve used for other projects. This book will show you which programming techniques you can use—and which you should avoid—when building web applications with this popular content management framework. Updated to cover both Drupal 7 and Drupal 8, the guidelines in this book demonstrate which programming practices conform to the "Drupal way" and which don’t. The book also serves as an excellent guide for Drupal 7 programmers looking to make the transition to Drupal 8. Get an overview of Drupal, including Drupal core and add-on modules and themes Learn Drupal’s basic programming principles, such as the ability to customize behavior and output with hooks Compare Drupal 7 and Drupal 8 programming methods, APIs, and concepts Discover common Drupal programming mistakes—and why hacking is one of them Explore specific areas where you can put your programming skills to work Learn about the new object-oriented Drupal 8 API, including plugins and services

Common Errors In Statistics And How To Avoid Them

Author: Phillip I. Good
Publisher: John Wiley & Sons
ISBN: 1118360117
Size: 24.59 MB
Format: PDF, ePub, Mobi
View: 4873
Download
Praise for Common Errors in Statistics (and How to Avoid Them) "A very engaging and valuable book for all who use statistics in any setting." —CHOICE "Addresses popular mistakes often made in data collection and provides an indispensable guide to accurate statistical analysis and reporting. The authors' emphasis on careful practice, combined with a focus on the development of solutions, reveals the true value of statistics when applied correctly in any area of research." —MAA Reviews Common Errors in Statistics (and How to Avoid Them), Fourth Edition provides a mathematically rigorous, yet readily accessible foundation in statistics for experienced readers as well as students learning to design and complete experiments, surveys, and clinical trials. Providing a consistent level of coherency throughout, the highly readable Fourth Edition focuses on debunking popular myths, analyzing common mistakes, and instructing readers on how to choose the appropriate statistical technique to address their specific task. The authors begin with an introduction to the main sources of error and provide techniques for avoiding them. Subsequent chapters outline key methods and practices for accurate analysis, reporting, and model building. The Fourth Edition features newly added topics, including: Baseline data Detecting fraud Linear regression versus linear behavior Case control studies Minimum reporting requirements Non-random samples The book concludes with a glossary that outlines key terms, and an extensive bibliography with several hundred citations directing readers to resources for further study. Presented in an easy-to-follow style, Common Errors in Statistics, Fourth Edition is an excellent book for students and professionals in industry, government, medicine, and the social sciences.