Poisson Regression with Survey Data

Abstract: We propose a way to remove the bias of a Poisson regression when the subjects are partially observed. In this paper we address this issue under certain assumptions about the missing-data generating process. We fix the total number of observed subjects and allow individual subjects to be observed randomly. This theme is relevant when a researcher is provided with a survey data not covering the whole population. A highlighting result is that if subjects are observed according to a random sampling without replacement, a Poisson distribution with sampling-ratio-adjusted mean is an asymptotically consistent model of the observed count variable. An innovative asymptotic regime is employed to derive the results.