Estimating measures of location is a fundamental statistical problem. The sample mean is not always a good choice to estimate location because it is not resistant to the influence of outliers. To treat this problem in a precise manner when nonnormality is present, we may use robust location estimates. In this work, we present a number of robust location functionals and deter- mine their breakdown point and influence function. We study robust location estimates such as the sample trimmed mean, the sample Winsorized mean and estimates based on symmetric quantiles. Confidence interval estimation and hypothesis testing are examined from a robust perspective. Both the one-sample case and the two-sample case are considered, the latter under two situations : independence and dependence. A few practical examples illustrate the study. Some R programmes are presented in this book.

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