Sufficient dimension reduction in multivariate regressions with categorical predictors

Haileab Hilafu and Xiangrong Yin

Computational Statistics & Data Analysis, 2013, vol. 63, issue C, 139-147

Abstract: In this paper, we present a novel sufficient dimension reduction method for multivariate regressions with categorical predictors. We adopt ideas from a previous work byChiaromonte et al. (2002) who proposed sufficient dimension reduction in regressions with categorical predictors and the work by Li et al. (2008) who proposed the projective-resampling idea to multivariate response problems. In addition, we incorporate a variable selection procedure. Simulation studies show the efficacy of our method. We present a real data analysis through our proposed method to discover new association between personal characteristics and dietary factors which influence plasma beta-carotene and retinol levels in human serum.

Keywords: Central subspace; Dimension reduction; Projective resampling; Sliced inverse regression; Variable selection (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:63:y:2013:i:c:p:139-147

DOI: 10.1016/j.csda.2013.02.014

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