6 papers
Asymptotic Distribution-Free Tests for Ultra-high Dimensional Parametric Regressions via Projected Empirical Processes and -value Combination
Falong Tan, Shan Tang, Lixing Zhu
This paper develops a novel methodology for testing the goodness-of-fit of sparse parametric regression models based on projected empirical processes and p-value combination, where…
A Two-Step Projection-Based Goodness-of-Fit Test for Ultra-High Dimensional Sparse Regressions
Falong Tan, Jie Liu, Heng Peng +1
This paper proposes a novel two-step strategy for testing the goodness-of-fit of parametric regression models in ultra-high dimensional sparse settings, where the predictor dimensi…
The conditionally studentized test for high-dimensional parametric regressions
Feng Liang, Chuhan Wang, jiaqi Huang +1
This paper studies model checking for general parametric regression models having no dimension reduction structures on the predictor vector. Using any U-statistic type test as an i…
Weighted residual empirical processes, martingale transformations, and model specification tests for regressions with diverging number of parameters
Falong Tan, Xu Guo, Lixing Zhu
This paper explores hypothesis testing for the parametric forms of the mean and variance functions in regression models under diverging-dimension settings. To mitigate the curse of…
Integrated conditional moment test and beyond: when the number of covariates is divergent
Falong Tan, Lixing Zhu
The classic integrated conditional moment test is a promising method for testing regression model misspecification. However, it severely suffers from the curse of dimensionality. T…
Estimation and adaptive-to-model testing for regressions with diverging number of predictors
Falong Tan, Lixing Zhu
The research described in this paper is motivated by model checking for parametric single-index models with diverging number of predictors. To construct a test statistic, we first…