论文检索

利用 MCMC 实现基于估计函数的推断AI 解读

Under a semiparametric or nonparametric setting, inferences about the unknown parameter are often made based on a nonsmooth estimating function. Resampling methods are quite handy for obtaining good approximations to the distribution of the consistent estimator when the estimating equation and its resampled counterparts are not difficult to solve numerically. In this article we propose a simple, flexible procedure that provides such approximations through the standard Markov…

利用删失回归模型评估 t 年生存者预测规则AI 解读

Suppose that we are interested in establishing simple but reliable rules for predicting future t-year survivors through censored regression models. In this article we present inference procedures for evaluating such binary classification rules based on various prediction precision measures quantified by the overall misclassification rate, sensitivity and specificity, and positive and negative predictive values. Specifically, under various working models, we derive consistent…

具有时变回归系数的Cox模型AI 解读

In the analysis of censored failure time observations, the standard Cox proportional hazards model assumes that the regression coefficients are time invariant. Often, these parameters vary over time, and the temporal covariate effects on the failure time are of great interest. In this article, following previous work of Cai and Sun, we propose a simple estimation procedure for the Cox model with time-varying coefficients based on a kernel-weighted partial likelihood…

不假定参数化误差分布的 Box–Cox 变换模型估计AI 解读

Box and Cox proposed a power transformation for the response variable that yields a linear regression model with normal error and constant variance. Inference procedures for the regression coefficients and transformation parameter under this model setting have been studied extensively. In this article we propose a simple semiparametric estimation method for the Box–Cox transformation model with no specific parametric assumption on the distribution of the error term. The…