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函数型删失分位数回归AI 解读

We propose a functional censored quantile regression model to describe the time-varying relationship between time-to-event outcomes and corresponding functional covariates. The time-varying effect is modeled as an unspecified function that is approximated via B-splines. A generalized approximate cross-validation method is developed to select the number of knots by minimizing the expected loss. We establish asymptotic properties of the method and the knot selection procedure.…

测量误差下删失分位数回归的平滑与校正得分方法AI 解读

Censored quantile regression is an important alternative to the Cox proportional hazards model in survival analysis. In contrast to the usual central covariate effects, quantile regression can effectively characterize the covariate effects at different quantiles of the survival time. When covariates are measured with errors, it is known that naively treating mismeasured covariates as error-free would result in estimation bias. Under censored quantile regression, we propose…

含长期存活群体的删失数据治愈率分位数回归AI 解读

Censored quantile regression offers a valuable complement to the traditional Cox proportional hazards model for survival analysis. Survival times tend to be right-skewed, particularly when there exists a substantial fraction of long-term survivors who are either cured or immune to the event of interest. For survival data with a cure possibility, we propose cure rate quantile regression under the common censoring scheme that survival times and censoring times are…

删失数据的幂变换线性分位数回归AI 解读

We propose a class of power-transformed linear quantile regression models for survival data subject to random censoring. The estimation procedure follows two sequential steps. First, for a given transformation parameter, we can easily obtain the estimates for the regression coefficients by minimizing a well-defined convex objective function. Second, we can estimate the transformation parameter based on a model discrepancy measure by constructing cumulative sum processes. We…