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R-estimatorsStatistical Analysis Techniques, Robust Estimators, Alternatives to OLS The three main classes of robust estimators are M, L and R. Robust estimators are resistant to outliers and when used in regression modelling, are robust to departures from the normality assumption. R-estimators involve ranking residuals. The rank of a sample is a mapping from n real numbers to the integers 1 through to n. The smallest number is given rank 1, the next smallest is given rank 2 etc. For example: ![]() The ranks are used to calculate weights. For example: ![]() When generating an R vector, ties need to be taken into account. A tie is when two values getting ranked are equal. For the weights to work, ![]() ![]() Jaeckel’s estimatorThis was proposed by Jaeckel in 1972. It involves finding the beta coefficient that minimises D:![]() He found that D is non-negative and beta is asymptotically normal with mean β. See also:RegressionM-estimators L-estimators |
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