Introduction to Robust Estimation and Hypothesis Testing

Introduction to Robust Estimating and Hypothesis Testing,Fifth Edition is a comprehensive guide to robust methods, offering improved techniques for dealing with outliers, skewed distribution curvature, and heteroscedasticity, with over 1700 R functions for practice.

Format: Paperback / softback
Length: 928 pages
Publication date: 15 February 2022
Publisher: Elsevier Science Publishing Co Inc

Introduction to Robust Estimating and Hypothesis Testing,Fifth Edition is a valuable guide for applying robust methods using user-friendly software. This renowned resource offers a comprehensive overview of modern robust techniques, including advanced strategies for handling outliers, skewed distribution curvature, and heteroscedasticity, which can significantly enhance power. The coverage spans techniques for comparing groups and assessing effect size, contemporary methods for comparing quantiles, and expanded regression methods for both parametric and nonparametric approaches. By utilizing data from real-world studies, the practical significance of these diverse methods is demonstrated. Furthermore, the book includes over 1700 R functions to facilitate comprehension and hands-on practice. Whether you are a researcher or a practitioner seeking reliable and effective statistical analysis, Introduction to Robust Estimating and Hypothesis Testing,Fifth Edition is an essential resource for your library.

Robust Estimating and Hypothesis Testing,Fifth Edition is a comprehensive and practical guide that provides an in-depth exploration of robust methods and their application in statistical analysis. This edition is designed to be accessible to a wide range of readers, including those with limited statistical expertise.

The book begins by introducing the concept of robustness and its importance in statistical inference. It explains how traditional statistical methods may be susceptible to biases and errors, particularly when data are not normally distributed or when there are outliers or influential observations. Robust methods, on the other hand, are designed to overcome these limitations and provide more reliable estimates and tests of statistical significance.

The fifth edition of this book is extensively revised and updated to reflect the latest developments in robust statistics. It includes coverage of a wide range of robust methods, including the bootstrap, empirical Bayes, and trimmed mean methods. Each method is explained in detail, with examples and applications drawn from real-world data sets.

The book also emphasizes the importance of hypothesis testing in statistical analysis. It provides a comprehensive introduction to hypothesis testing, including the definition of null and alternative hypotheses, the selection of appropriate test statistics, and the interpretation of test results. The book covers both parametric and nonparametric hypothesis testing, including the t-test, chi-square test, and nonparametric tests such as the Mann-Whitney U test and Kruskal-Wallis test.

In addition to its coverage of robust methods and hypothesis testing, the fifth edition of this book includes extensive examples and exercises to help readers apply the techniques to real-world data sets. These examples cover a wide range of fields, including economics, psychology, and health sciences, and provide readers with hands-on experience in applying robust statistical analysis.

One of the key strengths of this book is its user-friendly approach. The authors have designed the text to be clear and concise, with step-by-step instructions and explanations that make it easy for readers to follow. The book also includes numerous visual aids, such as figures and tables, to help illustrate the concepts and methods discussed.

Another notable feature of this book is its extensive use of R, a popular open-source statistical software package. R is widely used in the field of statistics and provides a powerful set of tools for robust statistical analysis. The fifth edition of this book includes over 1700 R functions that readers can use to implement the methods discussed in the text. This makes it an invaluable resource for those who want to learn and apply robust statistical analysis using R.

In conclusion, Robust Estimating and Hypothesis Testing,Fifth Edition is a comprehensive and practical guide that provides an in-depth exploration of robust methods and their application in statistical analysis. Whether you are a researcher, practitioner, or student, this book is an essential resource for your library. Its user-friendly approach, extensive examples, and extensive use of R make it an invaluable tool for anyone seeking to improve their statistical skills and knowledge.

Weight: 1418g
Dimension: 193 x 236 x 36 (mm)
ISBN-13: 9780128200988
Edition number: 5 ed