A journey in single steps: robust one-step $M$- estimation in linear regression.
We present a unified treatment of different types of one-step M-estimation in regression models which incorporates the Newton-Raphson, method of scoring and iteratively reweighted least squares forms of one-step estimator. We use higher order expansions to distinguish between the different forms of estimator and the effects of different initial estimators. We show that the Newton-Raphson form has better properties than the method of scoring form which, in turn, has better properties than the...[Show more]
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|Source:||Journal of Statistical Planning and Inference|
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