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协变量缺失时离散生命史数据分析的新方法AI 解读

Summary Regular censusing of wild animal populations produces data for estimating their annual survival. However, there can be missing covariate data; for instance time varying covariates that are measured on individual animals often contain missing values. By considering the transitions that occur from each occasion to the next, we derive a novel expression for the likelihood for mark–recapture–recovery data, which is equivalent to the traditional likelihood in the case…

鸟类环志回收模型中的边界估计AI 解读

SUMMARY A simple model for avian survival often leads to boundary estimates when the model is fitted to data by maximum likelihood. Data may result from ring recovery experiments alone, or in combination with a radio tracking investigation. A study of profile likelihoods enables the conditions under which boundary estimates occur to be completely specified. Fitting an incorrect model is shown to result in inconsistent estimators of all parameters, and an asymptotic certainty…

关于Seber环志回收数据模型的说明AI 解读

We provide simple explicit maximum-likelihood estimates for Seber's (1971) model for ring-recovery data, obtaining both the general solution, and the particular estimate satisfying Seber's constraint.

关于环志回收数据Seber模型的一则说明AI 解读

We provide simple explicit maximum-likelihood estimates for Seber's (1971) model for ring-recovery data, obtaining both the general solution, and the particular estimate satisfying Seber's constraint.

对角线缺失列联表中最大似然估计的迭代方法比较AI 解读

This paper considers maximum likelihood estimation for the parameters of quasiindepen-dence in a contingency table with a miiming or excluded diagonal. Two of the methods discussed derive from the em algorithm of Dempster, Laird & Rubin (1977), the other two methods being Newton-Raphson and iterative scaling. Iterative scaling is found, empirically, to be the least efficient method.

This paper considers maximum likelihood estimation for the parameters of quasiindepen-dence in a contingency table with a miiming or excluded diagonal. Two of the methods discussed derive from the em algorithm of Dempster, Laird & Rubin (1977), the other two methods being Newton-Raphson and iterative scaling. Iterative scaling is found, empirically, to be the least efficient method.