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参数线性约束下最大似然估计的受限 EM 算法AI 解读

The EM algorithm is one of the most powerful algorithms for obtaining maximum likelihood estimates for many incomplete-data problems. But when the parameters must satisfy a set of linear restrictions, the EM algorithm may be too complicated to apply directly. In this article we propose maximum likelihood estimation procedures under a set of linear restrictions for situations in which the EM algorithm could be used if there were no such restrictions on the parameters. We…

试验次数未观测时过度离散二项数据的两侧变换方法AI 解读

A common complication in analyzing binomial data is overdispersion, where the observed variation exceeds that predicted from the binomial distribution with parameters N and P. We consider the situation where N is not observed and variable. To estimate the regression parameters associated with covariates, we apply the transform-both-sides method. Based on the first-order asymptotic variance stabilizing transformation, we develop the arcsine transformation family indexed by a…