Asymptotic inference for semiparametric association models

Abstract

Association models for a pair of random elements $X$ and $Y$ (e.g., vectors) are considered which specify the odds ratio function up to an unknown parameter $\boldsθ$. These models are shown to be semiparametric in the sense that they do not restrict the marginal distributions of $X$ and $Y$. Inference for the odds ratio parameter $\boldsθ$ may be obtained from sampling either $Y$ conditionally on $X$ or vice versa. Generalizing results from Prentice and Pyke, Weinberg and Wacholder and Scott and Wild, we show that asymptotic inference for $\boldsθ$ under sampling conditional on $Y$ is the same as if sampling had been conditional on $X$. Common regression models, for example, generalized linear models with canonical link or multivariate linear, respectively, logistic models, are association models where the regression parameter $\boldsβ$ is closely related to the odds ratio parameter $\boldsθ$. Hence inference for $\boldsβ$ may be drawn from samples conditional on $Y$ using an association model.

Published in at http://dx.doi.org/10.1214/07-AOS572 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)