On Classification Based on Totally Bounded Classes of Functions when There are Incomplete Covariates
نویسندگان
چکیده
This article deals with the two-group classification problem, where the class conditional probability π(z) = P{Y = 1 | Z = z} belongs to a known class of functions F which is totally bounded with respect to the supremum norm. Given an -cover F of F , we consider kernel regression methods for constructing classifiers using members of F . A Horvitz-Thompsontype inverse weighting approach will be used to handle the presence of incomplete covariates in the data. Conditions under which the resulting classifiers are strongly consistent are also given.
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