论文检索

含高维干扰参数的广义线性模型检验AI 解读

Summary Generalized linear models often have high-dimensional nuisance parameters, as seen in applications such as testing gene-environment interactions or gene-gene interactions. In these scenarios, it is essential to test the significance of a high-dimensional subvector of the model’s coefficients. Although some existing methods can tackle this problem, they often rely on the bootstrap to approximate the asymptotic distribution of the test statistic, and are thus…

半参数优势比模型迭代算法收敛性的一则说明AI 解读

This paper points out an error in Davidov and Iliopoulos's (Biometrika100, 778–80) proof of convergence of an iterative algorithm for the proportional likelihood ratio model. It is shown that the iterative algorithm increases the likelihood in each iteration and converges under mild additional conditions when the odds ratio function is bounded.

半参数优势比模型的似然推断AI 解读

A flexible semiparametric odds ratio model has been proposed to unify and to extend both the log-linear model and the joint normal model for data with a mix of discrete and continuous variables. The semiparametric odds ratio model is particularly useful for analyzing biased sampling designs. However, statistical inference of the model has not been systematically studied when more than one nonparametric component is involved in the model. In this article, we study the maximum…

有偏抽样设计中参数可识别性与估计的统一框架AI 解读

Based on the odds ratio representation of a joint density, we propose a unified framework to study parameter identifiability in biased sampling designs. It is shown that most of these designs encountered in practice can be reformulated within the proposed framework and, as a result, the question of parameter identifiability can be largely clarified. Estimation of the identifiable parameters is considered and traditional results on the equivalence of the prospective and…

参数回归中缺失协变量的非参数与半参数模型AI 解读

Robustness of covariate modeling for the missing-covariate problem in parametric regression is studied under the missing-at-random assumption. For a simple missing-covariate pattern, nonparametric covariate model is proposed and is shown to yield a consistent and semiparametrically efficient estimator for the regression parameter. Total robustness is achieved in this situation. For more general missingcovariate patterns, a novel semiparametric modeling approach is proposed…

Cox回归中缺失协变量的双半参数方法AI 解读

The problem of nuisance covariate model specification is considered in Cox regression where the maximum semiparametric likelihood method is used to handle the missing covariates. A component of the covariates is modeled nonparametrically to achieve robustness against covariate model misspecification and to reduce the number of possibly intractable integrations involved in the parametric modeling of the covariates. The statistical properties of the proposed method are…

病例队列设计下比例优势回归的加权半参数似然方法AI 解读

The problem of fitting a proportional odds regression model to data from the case-cohort design proposed by Prentice is considered. A weighted semiparametric likelihood method is proposed. Under the proportional odds model, the maximum weighted-semiparametric likelihood estimators of both the regression parameter and the transformation function are shown to be consistent and normally distributed. The applicability of the weighted semiparametric likelihood method to the…

协变量缺失下的比例风险回归AI 解读

Nonparametric maximum likelihood (NPML) is used to estimate regression parameters in a proportional hazards regression model with missing covariates. The NPML estimator is shown to be consistent and asymptotically normally distributed under some conditions. EM type algorithms are applied to solve the maximization problem. Variance estimates of the regression parameters are obtained by a profile likelihood approach that uses EM-aided numerical differentiation. Simulation…