Abstract
This article studies the strong consistency of M-estimates in linear regression models directly from the minimization problem▪where X2. X2,… can be random observations of a p-dimensional random vector X, or that they are simply known nonrandom p-vectors. It is shown that the solution ▪ of this minimization problem converges with probability one to the true parameter ▪ under very general conditions on the function ρ and the sequence {(Xi′, Yi)}.
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