Introduction to robust estimation and hypothesis testing
著者
書誌事項
Introduction to robust estimation and hypothesis testing
Elsevier/Academic Press, c2017
4th ed
大学図書館所蔵 件 / 全7件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Includes bibliographical references and index
内容説明・目次
内容説明
Introduction to Robust Estimating and Hypothesis Testing, 4th Editon, is a 'how-to' on the application of robust methods using available software. Modern robust methods provide improved techniques for dealing with outliers, skewed distribution curvature and heteroscedasticity that can provide substantial gains in power as well as a deeper, more accurate and more nuanced understanding of data. Since the last edition, there have been numerous advances and improvements. They include new techniques for comparing groups and measuring effect size as well as new methods for comparing quantiles. Many new regression methods have been added that include both parametric and nonparametric techniques. The methods related to ANCOVA have been expanded considerably. New perspectives related to discrete distributions with a relatively small sample space are described as well as new results relevant to the shift function. The practical importance of these methods is illustrated using data from real world studies. The R package written for this book now contains over 1200 functions.
New to this edition
35% revised content
Covers many new and improved R functions
New techniques that deal with a wide range of situations
目次
1. Introduction2. A Foundation for Robust Methods3. Estimating Measures of Location and Scale4. Confidence Intervals in the One-Sample Case5. Comparing Two Groups6. Some Multivariate Methods7. One-Way and Higher Designs for Independent Groups8. Comparing Multiple Dependent Groups9. Correlation and Tests Of Independence10. Robust Regression11. More Regression Methods12. ANCOVA
「Nielsen BookData」 より