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Perhaps the major difficulty with robustizing the least squares approach is that both the dependent and the independent variables may be corrupted by gross errors. Thus, we rarely can be sure whether an outlying row xi. of the carrier matrix X correctly renders the location of a far-out observation, or whether its outlyingness has been caused by a gross recording error. And not all gross recording errors will show up as outlying rows; insidious examples are misrecorded values of the index i . In the literature of the past decades, much work has been devoted to robust regression models with random carriers X . But it does not seem that an adequate conceptual penetration has been achieved. In particular, unless one has a realistic idealized model underlying the generation of the rows of X (from which one then has small deviations), the concept of robustness would seem to be ill defined. In some cases, a treatment via robust covariancekorrelation matrices would seem to make more sense than the regression approach. See the discussion of these other approaches in Section 7.12. Apart from these difficulties, in view of the preceding section, we must expect that asymptotic regression theories run into problems, unless h = max hi + 0. Throughout the present and the following sections, we shall therefore assume that h is small. The discussion of how much smallness we may need will be taken up again in Section 7.9. Smallness of h for most practical purposes implies that we can act as if the xij are free of gross errors (note that a randomly misrecorded value of the index i has much the same effects as a gross error in yi). More precisely, error-free asp.net code 39 reader .NET Code-39 Barcode Reader for C#, VB.NET, ASP.NET Applications
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