Asymptotic Properties of Maximum Likelihood Estimates in the Mixed Poisson Model

This paper considers the asymptotic behavior of the maximum likelihood estimators (mle's) of the probabilities of a mixed Poisson distribution with a nonparametric mixing distribution. The vector of estimated probabilities is shown to converge in probability to the vector of mixed probabilities at rate $n^{1/2-\varepsilon}$ for any $\varepsilon > 0$ under a generalized $\chi^2$ distance function. It is then shown that any finite set of the mle's has the same joint limiting distribution as does the corresponding set of sample proportions when the support of the mixing distribution $G_0$ is an infinite set with a known upper bound and $G_0$ satisfies a certain condition at zero.This paper considers the asymptotic behavior of the maximum likelihood estimators (mle's) of the probabilities of a mixed Poisson distribution with a nonparametric mixing distribution. The vector of estimated probabilities is shown to converge in probability to the vector of mixed probabilities at rate $n^{1/2-\varepsilon}$ for any $\varepsilon > 0$ under a generalized $\chi^2$ distance function. It is then shown that any finite set of the mle's has the same joint limiting distribution as does the corresponding set of sample proportions when the support of the mixing distribution $G_0$ is an infinite set with a known upper bound and $G_0$ satisfies a certain condition at zero.

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笔记与分组

分类依据与纠错

Asymptotic Properties of Maximum Likelihood Estimates in the Mixed Poisson Model

代码、数据与补充材料

混合泊松模型中极大似然估计的渐近性质

原文摘要

同方向的基础文献

同方向的基础文献