Adaptive Regression by Yadolah Dodge

By Yadolah Dodge

Linear regression is a vital sector of facts, theoretical or utilized. there were a number of estimation equipment proposed and built for linear regression. each one has its personal aggressive area yet none is nice for all reasons. This manuscript makes a speciality of development of an adaptive mix of 2 estimation tools. the aim of such adaptive equipment is to aid clients make an aim selection and to mix fascinating houses of 2 estimators.

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X~, Y n ), leading to estimator Tn, which, being replaced by some anomalous data, lead to estimator T~ such that IITn - T~II = 00. The estimators with breakdown point 1/2 are thus desirable, and to develop such estimators was a goal of many statisticians in the past decade. Four of such estimators are the least median of squares (LMS), the least trimmed squares (LTS), the S-estimators, and the least trimmed sum of absolute deviations (LTA). The LMS minimizes the median of squares of residuals with respect to {3.

Chapter 4 of Birkes and Dodge (1993) is also devoted to the LAD regression. For more on theory and computation for LAD regression, see the conference proceedings edited by Dodge (1987a, 1992, 1997b) and the book by Bloomfield and Steiger (1983). 4b. S-PLUS calculates the LAD regression by means of the L1FIT; it is based on the Barrodale and Roberts (1973, 1974) algorithm, which is a version of the simplex algorithm for the linear programming problems. Koenker (1997) and Portnoy and Koenker (1997) developed a new algorithm based on the interior point method; for large n and moderate p it performs considerably faster than the L2 algorithm.

Proof. Denote where ej = (ejl, ... ,ejp)', ejk = 8Jk = I[j = k], j,k Rj (u) be the derivative from the right of R j , namely = 1, ... 27) Then Rj(u) is nondecreasing in u, and hence Rj(-7]) ~ Rj(O) ~ Rj(7]) for 7] > 0 and Rj ( -7]) ~ 0 and Rj (7]) 2 0 because R j (u) is minimized at u = O. 9) and the fact that at most p of Xi's could be equal to x;Tn(8) with positive probability (note that the distribution function F is continuous) . In the next step, we shall prove that I vn(Tn(8 0 ) - (3) 11= Op(l); in other words, that Tn(8 0 ) is a vn-consistent estimator of (3.

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