论文检索

存在隐藏混杂时过参数化模型的在线推断AI 解读

Abstract In this paper, we study online estimation and inference of regression coefficients in the presence of hidden confounders by leveraging over-parameterized models. Unlike existing offline approaches that rely on factor and sparse models, our closed-form estimator simultaneously removes hidden-confounder bias and is directly applicable to streaming data. Using tools from random matrix theory, we analyse phase transition phenomena in the variance of the coefficient…

含不可观测异质性的时空模型估计与自助法AI 解读

Proposed herein is a novel spatiotemporal model to characterize the unobserved heterogeneity across individuals using quantile-function-based correlated random effects and heteroscedastic innovations in a general framework. This model can be used to explore the influence of space-specific factors on latent effects at different quantile levels by controlling for spatiotemporal effects. A two-stage estimation procedure is introduced in which (i) the method of moments is used…

分位数回归模型的失拟检验AI 解读

Summary The paper novelly transforms lack-of-fit tests for parametric quantile regression models into checking the equality of two conditional distributions of covariates. Accordingly, by applying some successful two-sample test statistics in the literature, two tests are constructed to check the lack of fit for low and high dimensional quantile regression models. The low dimensional test works well when the number of covariates is moderate, whereas the high dimensional test…

分位数回归中变系数的估计与检验AI 解读

In this article, we establish a novel connection between the null hypothesis H0 on the coefficients and a rank-reducible form of the varying coefficient model in quantile regression. We use B-splines to approximate the varying coefficients in the rank-reducible model, and make use of the fact that the null hypothesis H0 implies a unidimensional structure of a transformed coefficient matrix for the B-spline basis functions. By evaluating the unidimensional structure, we…