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参数正交性与近似条件推断AI 解读

SUMMARY We consider inference for a scalar parameter Ψ in the presence of one or more nuisance parameters. The nuisance parameters are required to be orthogonal to the parameter of interest, and the construction and interpretation of orthogonalized parameters is discussed in some detail. For purposes of inference we propose a likelihood ratio statistic constructed from the conditional distribution of the observations, given maximum likelihood estimates for the nuisance…

比例风险回归的影响函数AI 解读

Influence functions for the regression parameters in the proportional hazards model are presented. It is suggested that empirical influence functions, computed for each observation and each covariate, can be useful in an informal way to identify influential observations. This is illustrated on the Stanford heart transplant data and two other examples.

删失数据下的相对风险估计AI 解读

We investigate a class of nonparametric estimators of relative risk in the two-sample case of the proportional hazards model for censored data. The asymptotic distribution of these estimators is derived using influence functions. The optimal estimator in this class has the same influence functions and the same asymptotic distribution as the maximum partial likelihood estimator of Cox (1972). The behavior of the influence functions is discussed briefly, and the last section…

基于部分删失数据的卷积估计AI 解读

An estimator is proposed for the convolution of distribution functions, based on samples with some right-censored observations. The asymptotic variance of the estimator is calculated, and the method is illustrated with a set of cancer data.

利用部分删失数据估计分布卷积AI 解读

An estimator is proposed for the convolution of distribution functions, based on samples with some right-censored observations. The asymptotic variance of the estimator is calculated, and the method is illustrated with a set of cancer data.