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稀疏高维加性模型的估计与一致推断AI 解读

We develop a novel method to construct uniformly valid confidence bands for a nonparametric component f1 in the sparse additive model Y=f1(X1)+…+fp(Xp)+ɛ in a high-dimensional setting. Our method integrates sieve estimation into a high-dimensional Z-estimation framework, facilitating the construction of uniformly valid confidence bands for the target component f1. To form these confidence bands, we employ a multiplier bootstrap procedure. Additionally, we provide rates for…

高维环境下基于 L2 提升的处理效应估计与推断AI 解读

Empirical researchers are increasingly faced with rich data sets containing many controls or instrumental variables, making it essential to choose an appropriate approach to variable selection. In this paper, we provide results for valid inference after post- or orthogonal L2-boosting is used for variable selection. We consider treatment effects after selecting among many control variables and instrumental variable models with potentially many instruments. To achieve this,…

高维变换模型AI 解读

Transformation models are a very important tool for applied statisticians and econometricians. In many applications, the dependent variable is transformed so that homogeneity or normal distribution of the error holds. In this article, we analyze transformation models in a high-dimensional setting, where the set of potential covariates is large. We propose an estimator for the transformation parameter and we show that it is asymptotically normally distributed using an…