On post dimension reduction statistical inference

Kyongwon Kim, Bing Li, Zhou Yu, Lexin Li

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


The methodologies of sufficient dimension reduction have undergone extensive developments in the past three decades. However, there has been a lack of systematic and rigorous development of post dimension reduction inference, which has seriously hindered its applications. The current common practice is to treat the estimated sufficient predictors as the true predictors and use them as the starting point of the downstream statistical inference. However, this naive inference approach would grossly overestimate the confidence level of an interval, or the power of a test, leading to the distorted results. In this paper, we develop a general and comprehensive framework of post dimension reduction inference, which can accommodate any dimension reduction method and model building method, as long as their corresponding influence functions are available. Within this general framework, we derive the influence functions and present the explicit post reduction formulas for the combinations of numerous dimension reduction and model building methods. We then develop post reduction inference methods for both confidence interval and hypothesis testing. We investigate the finite-sample performance of our procedures by simulations and a real data analysis.

Original languageEnglish (US)
Pages (from-to)1567-1592
Number of pages26
JournalAnnals of Statistics
Issue number3
StatePublished - Jun 2020

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'On post dimension reduction statistical inference'. Together they form a unique fingerprint.

Cite this