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具有乘法分量的比例风险模型的光滑回代估计AI 解读

Smooth backfitting has proven to have a number of theoretical and practical advantages in structured regression. By projecting the data down onto the structured space of interest smooth backfitting provides a direct link between data and estimator. This article introduces the ideas of smooth backfitting to survival analysis in a proportional hazard model, where we assume an underlying conditional hazard with multiplicative components. We develop asymptotic theory for the…

操作时间与样本内密度预测AI 解读

In this paper, we consider a new structural model for in-sample density forecasting. In-sample density forecasting is to estimate a structured density on a region where data are observed and then reuse the estimated structured density on some region where data are not observed. Our structural assumption is that the density is a product of one-dimensional functions with one function sitting on the scale of a transformed space of observations. The transformation involves…

样本内密度预测的渐近理论AI 解读

This paper generalizes recent proposals of density forecasting models and it develops theory for this class of models. In density forecasting, the density of observations is estimated in regions where the density is not observed. Identification of the density in such regions is guaranteed by structural assumptions on the density that allows exact extrapolation. In this paper, the structural assumption is made that the density is a product of one-dimensional functions. The…

具有灵活协变量效应的半参数生存模型AI 解读

A semiparametric hazard model with parametrized time but general covariate dependency is formulated and analyzed inside the framework of counting process theory. A profile likelihood principle is introduced for estimation of the parameters: the resulting estimator is $n^{1/2}$-consistent, asymptotically normal and achieves the semiparametric efficiency bound. An estimation procedure for the nonparametric part is also given and its asymptotic properties are derived. We…

非参数标记依赖风险率模型中的核估计AI 解读

We introduce a new kernel hazard estimator in a nonparametric model where the stochastic hazard depends on the current value of time and on the current value of a time dependent covariate or marker. We establish the pointwise and global convergence of our estimator.

基于标记过程的一致预测规则框架AI 解读

Recently interest has developed regarding the statistical properties and uses of marker processes in the context of the analysis of failure time data or survival analysis. A marker process is a stochastic process that acts as a time dependent covariate that is internal to the unit under study in the language of Kalbfleisch & Prentice (1980). As such the sample path of the process up to a certain point in time may carry information about the subsequent hazard for failure.…

基于标记过程的一致预测规则框架AI 解读

Recently interest has developed regarding the statistical properties and uses of marker processes in the context of the analysis of failure time data or survival analysis. A marker process is a stochastic process that acts as a time dependent covariate that is internal to the unit under study in the language of Kalbfleisch & Prentice (1980). As such the sample path of the process up to a certain point in time may carry information about the subsequent hazard for failure.…