Dimension reduction based on conditional multiple index density function

Jun Zhang, Baohua He, Tao Lu, Songqiao Wen

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In this paper, a dimension reduction method is proposed by using the first derivative of the conditional density function of response given predictors. To estimate the central subspace, we propose a direct methodology by taking expectation of the product of predictor and kernel function about response, which helps to capture the directions in the conditional density function. The consistency and asymptotic normality of the proposed estimation methodology are investigated. Furthermore, we conduct some simulations to evaluate the performance of our proposed method and compare with existing methods, and a real data set is analyzed for illustration.

Original languageEnglish
Pages (from-to)851-872
Number of pages22
JournalBrazilian Journal of Probability and Statistics
Volume32
Issue number4
DOIs
StatePublished - Nov 2018

Keywords

  • Central subspace
  • Conditional density function
  • Dimensional reduction
  • Kernel function

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