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or, after taking derivatives, we solve (7.39) with $ = p . If p is convex, the two approaches are essentially equivalent; otherwise the selection of the best solution of (7.39) may create problems. These equations describe the plain vanilla regression M-estimates. They have been much maligned for having zero breakdown point with regard to leverage points, and some comments are in order. Quite apart from the technical problems with asymptotic theory caused by non-small values of h, the desirability of safegarding against leverage points in an automated fashion has been overrated, at least in my opinion. Repeating the remarks made in Section 7.1, I believe that leverage points ordinarily should be dealt with through diagnostics and human judgment, rather than through blind robust procedures. It appears that M-estimates offer enough flexibility and are by far the easiest to cope with, simultaneously, with regard to computation, asymptotic theory, and intuitive interpretation; moreover, the step from (7.3) to (7.38) is easily explainable to nonstatisticians also. The M-estimate approach is not a panacea (is there such a thing in statistics ), but it is easy to understand, practicable (i.e., easy to compute), and considerably safer than classical least squares. And it is the only robust regression estimate whose asymptotic behavior we believe to understand in fair detail (see the next section). Apart from brief comments on R- and L-estimates (see below), we therefore shall restrict ourselves to M-estimates of regression in the next sections and return to other approaches only in Section 7.12. We denote the ith residual by (7.40) asp.net gs1 128 VB.NET GS1-128(EAN-128) Reader SDK to read, scan ... - OnBarcode
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