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非参数条件分位数导数函数估计中的最优平滑AI 解读

Marginal effect in nonparametric quantile regression is of special interest as it quantitatively measures how one unit change in explanatory variable heterogeneously affects dependent variable ceteris paribus at distinct quantiles. In this paper, we propose a data-driven bandwidth selection procedure based on the gradient of an unknown quantile regression function. Our method delivers the bandwidth with the oracle property in the sense that it is asymptotically equivalent to…

高维工具变量回归的正则化方法及遗传基因组学应用AI 解读

In genetical genomics studies, it is important to jointly analyze gene expression data and genetic variants in exploring their associations with complex traits, where the dimensionality of gene expressions and genetic variants can both be much larger than the sample size. Motivated by such modern applications, we consider the problem of variable selection and estimation in high-dimensional sparse instrumental variables models. To overcome the difficulty of high…