ABSTRACT

Robust statistical methods were developed to supplement the classical procedures when the data violate classical assumptions. They are ideally suited to applied research across a broad spectrum of study, yet most books on the subject are narrowly focused, overly theoretical, or simply outdated. Robust Statistical Methods with R provides a systemati

chapter |4 pages

Introduction

chapter 1|22 pages

Mathematical tools of robustness

chapter 2|16 pages

Basic characteristics of robustness

chapter 3|42 pages

Robust estimators of real parameter

chapter 4|44 pages

Robust estimators in linear model

chapter 5|12 pages

Multivariate location model

chapter 7|18 pages

Some goodness-of-fit tests