Sufficient dimension reduction constrained through sub-populations

Elias Al-Najjar and Kofi P. Adragni

Computational Statistics & Data Analysis, 2017, vol. 111, issue C, 131-144

Abstract: Most methodologies for sufficient dimension reduction (SDR) in regression are limited to continuous predictors, although many data sets do contain both continuous and categorical variables. Application of these methods to regressions that include qualitative predictors such as gender or species may be inappropriate. Regressions that include a set of qualitative predictors W in addition to a vector X of many-valued predictors and a response Y are considered. Using principal fitted components (PFC) models, a likelihood-based SDR method, a sufficient dimension reduction of X that is constrained through the sub-populations established by W is sought. An estimator of the sufficient reduction subspace is provided and its use is demonstrated through applications.

Keywords: Dimension reduction; Principal fitted components; Partial reduction; Principal components (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:111:y:2017:i:c:p:131-144

DOI: 10.1016/j.csda.2017.02.008

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