Abstract

this paper we provide a method for constructing such estimating functions. When applied to longitudinal data, this handles covariates and random drop-out satisfactorily, as does partial likelihood; it avoids distributional assumptions, as does generalized estimating equations. It is obtained by projecting the partial score function onto a collection of Hilbert spaces with inner product specified by conditional moments, conditioned on nested events. We also demonstrate, within a prequential frame of reference (Dawid 1984, 1991), that the estimating function is optimal among the largest collection of estimating functions that can be described by the postulated conditional moments.

Citations