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http://dx.doi.org/10.5351/CSAM.2017.24.1.097

Model-based inverse regression for mixture data  

Choi, Changhwan (Department of Statistics, Sungkyunkwan University)
Park, Chongsun (Department of Statistics, Sungkyunkwan University)
Publication Information
Communications for Statistical Applications and Methods / v.24, no.1, 2017 , pp. 97-113 More about this Journal
Abstract
This paper proposes a method for sufficient dimension reduction (SDR) of mixture data. We consider mixture data containing more than one component that have distinct central subspaces. We adopt an approach of a model-based sliced inverse regression (MSIR) to the mixture data in a simple and intuitive manner. We employed mixture probabilistic principal component analysis (MPPCA) to estimate each central subspaces and cluster the data points. The results from simulation studies and a real data set show that our method is satisfactory to catch appropriate central spaces and is also robust regardless of the number of slices chosen. Discussions about root selection, estimation accuracy, and classification with initial value issues of MPPCA and its related simulation results are also provided.
Keywords
dimension reduction; sliced inverse regression; mixture modeling; principal component analysis; probability model;
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