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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.
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.