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随机样本量下比例参数的极小极大估计AI 解读

We study the problem of how to estimate the parameters of a multivariate hypergeometric distribution when the sample size n is assumed to be an ancillary statistic. The minimax estimator for squared error loss is given. This estimator differs from the well-known minimax estimator for fixed n. Furthermore, that classical estimator is shown to be not even admissible when n is random.

We study the problem of how to estimate the parameters of a multivariate hypergeometric distribution when the sample size n is assumed to be an ancillary statistic. The minimax estimator for squared error loss is given. This estimator differs from the well-known minimax estimator for fixed n. Furthermore, that classical estimator is shown to be not even admissible when n is random.