Strong Consistency of M-Estimates for the Linear Model.

Abstract

Let (sub 1),...,(sub n),... be i.i.d. observations of a random vector (X,Y) were Y is one-dimensional and X may be multi-dimensional. Suppose that the regression of Y to X, in some sense, is a linear function alpha sub o + beta sub o. It is desired to estimate the unknown parameters alpha sub o, beta sub o, using the observations (sub 1),...(sub n,). A much discussed class of estimates is the so-called M-estimate, which takes the solution of a certain minimization problem as the estimator. Here rho is a properly selected function defined over R' = (infinity). (Keywords: linear models).

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1987
Accession Number
ADA185487

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  • X. R. Chen
  • Yeuhua Wu

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  • University of Pittsburgh

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  • Mathematics

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  • Statistical inference.