Introduction to Robust Estimating and Hypothesis Testing, 4th Edition, (PDF) is a ‘how-to’ on the use of robust methods using available software. Modern robust methods offer improved techniques for dealing with outliers, skewed distribution curvature, and heteroscedasticity that can give substantial gains in power in addition to a deeper, more accurate, and more nuanced understanding of data. Since the previous edition, there have been several advances and improvements. They include new methods for comparing groups and measuring effect size along with new methods for comparing quantiles. Many new regression methods have been included that contain both parametric and nonparametric techniques. The techniques related to ANCOVA have been expanded considerably. New perspectives associated to discrete distributions with a comparatively small sample space are described besides new results relevant to the shift function. The practical importance of these methods is shown using data from real-world studies. The R package written for this ebook now contains more than 1200 functions.
New to this 4e edition:
- 35% revised content
- Features newest rank-based methods
- Includes latest improvements in ANOVA
- Describes and illustrated easy to use software
- Includes many new and improved R functions
- New techniques that deal with an extensive range of situations
- Extensive revisions to include the latest developments in robust regression
NOTE: The product only includes the ebook Introduction to Robust Estimating and Hypothesis Testing, 4th Editon, in PDF. No access codes are included.
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