Bayesian inverse regression for supervised dimension reduction with small datasets

Research output: Contribution to journalArticlepeer-review

Authors

Colleges, School and Institutes

External organisations

  • Shanghai Jiao Tong University
  • Purdue University

Abstract

We consider supervised dimension reduction problems, namely to identify a low dimensional projection of the predictors x which can retain the statistical relationship between x and the response variable y. We follow the idea of the sliced inverse regression (SIR) and the sliced average variance estimation (SAVE) type of methods, which is to use the statistical information of the conditional distribution π(x|y) to identify the dimension reduction (DR) space. In particular we focus on the task of computing this conditional distribution without slicing the data. We propose a Bayesian framework to compute the conditional distribution where the likelihood function is obtained using the Gaussian process regression model. The conditional distribution π(x|y) can then be computed directly via Monte Carlo sampling. We then can perform DR by considering certain moment functions (e.g. the first or the second moment) of the samples of the posterior distribution. With numerical examples, we demonstrate that the proposed method is especially effective for small data problems.

Bibliographic note

Funding Information: XC and JL were partially supported by the National Natural Science Foundation of China under grant number 11301337

Details

Original languageEnglish
Pages (from-to)2817-2832
Number of pages16
JournalJournal of Statistical Computation and Simulation
Volume91
Issue number14
Early online date8 Apr 2021
Publication statusPublished - 8 Apr 2021

Keywords

  • Bayesian inference, covariance operator, dimension reduction, Gaussian process, inverse regression, Dimension reduction, sliced inverse regression, supervised learning, Monte Carlo simulation, Statistics, Probability and Uncertainty, Modelling and Simulation, Statistics and Probability, Applied Mathematics