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Uniform convergence of the empirical cumulative distribution function under informative selection from a finite population. (English) Zbl 1329.62053
Summary: Consider informative selection of a sample from a finite population. Responses are realized as independent and identically distributed (i.i.d.) random variables with a probability density function (p.d.f.) \(f\), referred to as the superpopulation model. The selection is informative in the sense that the sample responses, given that they were selected, are not i.i.d. \(f\). In general, the informative selection mechanism may induce dependence among the selected observations. The impact of such dependence on the empirical cumulative distribution function (c.d.f.) is studied. An asymptotic framework and weak conditions on the informative selection mechanism are developed under which the (unweighted) empirical c.d.f. converges uniformly, in \(L_2\) and almost surely, to a weighted version of the superpopulation c.d.f. This yields an analogue of the Glivenko-Cantelli theorem. A series of examples, motivated by real problems in surveys and other observational studies, shows that the conditions are verifiable for specified designs.

MSC:
62D05 Sampling theory, sample surveys
60F25 \(L^p\)-limit theorems
62G20 Asymptotic properties of nonparametric inference
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