Sufficient Dimension Reduction Through Independence and Conditional Mean Independence Measures

Yuexiao Dong ()
Additional contact information
Yuexiao Dong: Temple University, Department of Statistical Science

A chapter in Festschrift in Honor of R. Dennis Cook, 2021, pp 167-180 from Springer

Abstract: Abstract We propose a unified framework for sufficient dimension reduction through independence and conditional mean independence measures. When the interest is the conditional distribution of Y given X, α-distance covariance is used to recover the central space. If the focus is the conditional mean of Y given X, the central mean space can be estimated through α-martingale difference divergence. Compared with existing estimators based on the distance covariance which recover the central space, the new estimators are more accurate when the target is the central mean space. By choosing α smaller than one, the new estimators outperform existing estimators when the predictor distribution is heavy-tailed and when there is data contamination.

Keywords: Central mean space; Central space; Distance covariance; Martingale difference divergence (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-69009-0_8

Ordering information: This item can be ordered from
http://www.springer.com/9783030690090

DOI: 10.1007/978-3-030-69009-0_8

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().