Inference about parameters in Binomial-Poisson distribution with additional incomplete data

Suisui Che, Kai Huang and Jie Mi

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 11, 3206-3222

Abstract: This article considers the distribution of Binomial-Poisson random vector which has two components and includes two parameters: one is the rate of a Poisson distribution, the other is the proportion in a Binomial distribution. The inference about the two parameters is usually made based on only paired observations. However, the number of paired observations is, in general, not large enough because of either technical difficulty or budget limitation, and so one can not make efficient inferences with only paired data. Instead, it is often much easier and not too costly to have incomplete observation on only one component independently. In this article we will combine both the paired complete data and unpaired incomplete data for estimating the two parameters. The performances of various estimators are compared both analytically and numerically. It is observed that fully using the unpaired incomplete data can always improve the inference, and the improvement is very significant in the case when there are only a few paired complete observations.

Date: 2016
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DOI: 10.1080/03610926.2014.901367

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