论文检索

二项分布参数的经验贝叶斯估计AI 解读

A smooth empirical Bayes estimator for a binomial parameter is derived. The risk in using this estimator is compared by simulation with that of eight other binomial estimators. For those estimators not having a simple closed form risk, suitable regression equations on the simulation parameters are obtained. These equations are used to identify the regions of superiority of each of the estimators. A numerical example is provided.

二项分布参数的经验贝叶斯估计AI 解读

A smooth empirical Bayes estimator for a binomial parameter is derived. The risk in using this estimator is compared by simulation with that of eight other binomial estimators. For those estimators not having a simple closed form risk, suitable regression equations on the simulation parameters are obtained. These equations are used to identify the regions of superiority of each of the estimators. A numerical example is provided.

一种连续经验贝叶斯平滑技术AI 解读

Maritz (1966) and Lemon & Krutchkoff (1969) each describe discrete empirical Bayes smoothing techniques. These techniques essentially attempt to approximate the prior distribution function. Here a continuous smoothing technique which is based on a smooth and continuous approximation to the prior density function is presented. Results from a Monte Carlo study of the Poisson distribution are reported which show that the continuous smoothing technique has desirable small-sample…

一种连续经验贝叶斯平滑技术AI 解读

Maritz (1966) and Lemon & Krutchkoff (1969) each describe discrete empirical Bayes smoothing techniques. These techniques essentially attempt to approximate the prior distribution function. Here a continuous smoothing technique which is based on a smooth and continuous approximation to the prior density function is presented. Results from a Monte Carlo study of the Poisson distribution are reported which show that the continuous smoothing technique has desirable small-sample…