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两样本生存数据中短期与长期风险比的半参数分析AI 解读

Standard approaches to semiparametric modelling of two-sample survival data are not appropriate when the two survival curves cross. We introduce a two-sample model that accommodates crossing survival curves. The two scalar parameters of the model have the interpretations of being the short-term and long-term hazard ratios respectively. The time-varying hazard ratio is expressed semiparametrically by the two scalar parameters and an unspecified baseline distribution. The new…

基于加权经验生存函数与风险函数的删失中位数回归AI 解读

For median regression models that regress the median of the survival time or a transform thereof on the covariates, some semi-parametric estimators that include the intercept component are introduced when the survival time may be censored. These new median regression estimators do not require estimating the censoring distributions. They can be viewed as an extension of the sample median to the censored regression model. These estimators are based on some weighted empirical…

比例优势回归模型中的半参数推断AI 解读

For fitting the proportional odds regression model with right-censored survival times, we introduce some weighted empirical odds functions. These functions are solutions of some self-consistency equations and have a nice martingale representation. From these functions, several classes of new regression estimators, such as the pseudo–maximum likelihood estimator, martingale residual-based estimators, and minimum distance estimators, are derived. These estimators have…

比例优势回归模型的半参数推断AI 解读

For fitting the proportional odds regression model with right-censored survival times, we introduce some weighted empirical odds functions. These functions are solutions of some self-consistency equations and have a nice martingale representation. From these functions, several classes of new regression estimators, such as the pseudo–maximum likelihood estimator, martingale residual-based estimators, and minimum distance estimators, are derived. These estimators have…

删失回归中扩展的加权对数秩估计函数AI 解读

In the censored regression model, some regression estimators are introduced using weighted integrals of the log-rank estimating functions. Their limiting covariance matrices do not involve the error density and can be reliably estimated. Inference can then be easily obtained. Some of these estimators have high asymptotic efficiency at some important submodels. Some lack-of-fit tests are derived that require little extra computing time. These tests are asymptotically normal…

乘积极限估计量的推广及其在删失回归中的应用AI 解读

The product-limit estimator (PLE) and weighted empirical processes are two important ingredients of almost any censored regression analysis. A link between them is provided by the generalized PLEs introduced in this paper. These generalized PLEs are the product-limit integrals of the empirical cumulative hazard function estimators in which weighted empirical processes are used to replace the standard empirical processes. The weak convergence and some large sample…

关于 Kaplan–Meier 估计量的若干不等式AI 解读

In this paper we consider the product-limit estimator of the survival distribution function in the context of independent but nonidentically distributed censoring times. An upper bound on the mean square increment of the stopped Kaplan-Meier process is obtained. Also, a representation is given for the ratio of the survival distribution function to the product-limit estimator as the product of a bounded process and a martingale. From this representation bounds on the mean…

随机删失模型中的最小 Hellinger 距离参数估计AI 解读

This paper discusses the minimum Hellinger distance estimation (MHDE) of the parameter that gives the "best fit" of a parametric family to a density when the data are randomly censored. In studying the MHDE, the tail behavior of the product-limit (P-L) process is investigated, and the weak convergence of the process on the real line is established. An upper bound on the mean square increment of the normalized P-L process is also obtained. With these results, the asymptotic…