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超高维异质数据的条件分位数筛选AI 解读

To accommodate the heterogeneity that is often present in ultrahigh-dimensional data, we propose a conditional quantile screening method, which enables us to select features that contribute to the conditional quantile of the response given the covariates. The method can naturally handle censored data by incorporating a weighting scheme through redistribution of the mass to the right; moreover, it is invariant to monotone transformation of the response and requires…

聚类失效时间线性回归的广义矩估计：引文更正AI 解读

Although Qu et al. (2000) was cited in § 1 of our paper, it should also have been mentioned as the source of the basis representation of the inverse correlation matrix following our equation (3). Moreover our Theorem 3 rests on arguments similar to those leading to Theorem 1 of Qu et al. (2000). We thank Dr Qu for pointing this out. ‘A group bridge approach for variable selection’ BY JIAN HUANG, SHUANGGE MA, HUILIANG XIE AND CUN-HUI ZHANG [email protected][email…

聚类失效时间数据线性回归的广义矩估计AI 解读

We propose a generalized method of moments approach to the accelerated failure time model with correlated survival data. We study the semiparametric rank estimator using martingale-based moments. We circumvent direct estimation of correlation parameters by concatenating the moments and minimizing a quadratic objective function. We establish the consistency and asymptotic normality of the parameter estimators, and derive the limiting distribution of the objective function. We…

多元失效时间数据的加性风险模型AI 解读

Marginal additive hazards models are considered for multivariate survival data in which individuals may experience events of several types and there may also be correlation between individuals. Estimators are proposed for the parameters of such models and for the baseline hazard functions. The estimators of the regression coeffcients are shown asymptotically to follow a multivariate normal distribution with a sandwich-type covariance matrix that can be consistently…