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删失加速失效时间模型秩估计量的经验似然分析AI 解读

We use the empirical likelihood method to derive a test and thus a confidence interval based on the rank estimators of the regression coefficient in the accelerated failure time model. Standard chi-squared distributions are used to calculate the p-value and to construct the confidence interval. Simulations and examples show that the chi-squared approximation to the distribution of the log empirical likelihood ratio performs well, and has some advantages over the existing…

非线性回归的渐近理论AI 解读

Introduction. 1. Consistency. 2. Approximation by a Normal Distribution. 3. Asymptotic Expansions Related to the Least Squares Estimator. 4. Geometric Properties of Asymptotic Expansions. Appendix: I: Subsidiary Facts. II: List of Principal Notations. Commentary. Bibliography. Index.

右删失线性回归分析中的分层使用AI 解读

We study two modified synthetic data least-squares estimation methods for linear regression models with right censored response variables, unspecified residual distributions and random censoring variables which may not be i.i.d. These methods are the result of an investigation into the use of stratification. We conclude that stratification should be used whether or not the censoring variables are dependent on the covariates. We give the asymptotic results of the estimators…

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删失生存数据“合成数据”回归估计量的渐近正态性AI 解读

This article studies the large sample behavior of the censored data least squares estimator derived from the synthetic data method proposed by Leurgans and Zheng. The asymptotic distributions are derived by representing the estimator as a martingale plus a higher-order remainder term. Recently developed counting process techniques are used. The results are then compared to the censored regression estimator of Koul, Susarla and Van Ryzin.

右删失线性模型中的 M 估计AI 解读

We propose a method of obtaining an M-estimator in a linear model when the responses are subject to right censoring. The central limit theorem for the estimator using squared error loss, i.e. least squares, is derived using counting process martingale techniques. The estimation method is applied to the Stanford heart transplant data for illustration.

独立但不同分布随机变量的 Kaplan–Meier 估计量若干性质AI 解读

In this paper we study the Kaplan-Meier estimator in the case where survival and censoring times are not all i.i.d. We prove several results which are analogous to those shown by van Zuijlen in the complete data case.