Model-Free Statistical Inference on High-Dimensional Data

Xu Guo, Runze Li, Zhe Zhang, Changliang Zou

Research output: Contribution to journalArticlepeer-review


This article aims to develop an effective model-free inference procedure for high-dimensional data. We first reformulate the hypothesis testing problem via sufficient dimension reduction framework. With the aid of new reformulation, we propose a new test statistic and show that its asymptotic distribution is (Formula presented.) distribution whose degree of freedom does not depend on the unknown population distribution. We further conduct power analysis under local alternative hypotheses. In addition, we study how to control the false discovery rate of the proposed (Formula presented.) tests, which are correlated, to identify important predictors under a model-free framework. To this end, we propose a multiple testing procedure and establish its theoretical guarantees. Monte Carlo simulation studies are conducted to assess the performance of the proposed tests and an empirical analysis of a real-world dataset is used to illustrate the proposed methodology. Supplementary materials for this article are available online including a standardized description of the materials available for reproducing the work.

Original languageEnglish (US)
JournalJournal of the American Statistical Association
StateAccepted/In press - 2024

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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