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给定协变量的高维分位数关联网络的错误发现率控制AI 解读

Summary Motivated by gene coexpression pattern analysis, we propose a novel sample quantile contingency (SQUAC) statistic to infer quantile associations conditioning on covariates. It features enhanced flexibility in handling variables with both arbitrary distributions and complex association patterns conditioning on covariates. We first derive its asymptotic null distribution, and then develop a multiple-testing procedure based on the SQUAC statistic to test simultaneously…

调整半竞争风险所致依赖删失的分位数回归AI 解读

Summary We study quantile regression when the response is an event time subject to potentially dependent censoring. We consider the semicompeting risks setting, where the time to censoring remains observable after the occurrence of the event of interest. Although such a scenario frequently arises in biomedical studies, most of current quantile regression methods for censored data are not applicable because they generally require the censoring time and the event time to be…

分位数关联回归模型AI 解读

It is often important to study the association between two continuous variables. In this work, we propose a novel regression framework for assessing conditional associations on quantiles. We develop general methodology which permits covariate effects on both the marginal quantile models for the two variables and their quantile associations. The proposed quantile copula models have straightforward interpretation, facilitating a comprehensive view of association structure…

评估有序与连续测量间广义一致性的框架AI 解读

Conventional agreement studies have been confined to addressing the sense of reproducibility, and therefore are limited to assessing measurements on the same scale. In this work, we propose a new concept, called "broad sense agreement," which extends the classical framework of agreement to evaluate the capability of interpreting a continuous measurement in an ordinal scale. We present a natural measure for broad sense agreement. Nonparametric estimation and inference…