TY - JOUR
T1 - Nonparametric mixture of regression models
AU - Huang, Mian
AU - Li, Runze
AU - Wang, Shaoli
N1 - Funding Information:
Mian Huang is Associate Professor, School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, P. R. China (E-mail: [email protected]). Runze Li is the corresponding author and Distinguished Professor, Department of Statistics and The Methodology Center, The Pennsylvania State University, University Park, PA 16802-2111 (E-mail: [email protected]). Shaoli Wang is Associate Professor, School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, P. R. China (E-mail: [email protected]). Huang’s and Wang’s research is supported by a funding through Projet 211 Phase 4 of SHUFE, and Shanghai Leading Academic Discipline Project, B803. Li’s research was supported by National Institute on Drug Abuse (NIDA) grants R21 DA024260 and P50-DA10075 and National Natural Science Foundation of China grant 11028103. The authors thank the editor, the AE, and the reviewers for their constructive comments, which have led to a dramatic improvement of the earlier version of this article. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or NIDA.
PY - 2013
Y1 - 2013
N2 - Motivated by an analysis of U.S. house price index (HPI) data, we propose nonparametric finite mixture of regression models.We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression.We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the U.S. HPI data is illustrated for the proposed methodology.
AB - Motivated by an analysis of U.S. house price index (HPI) data, we propose nonparametric finite mixture of regression models.We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression.We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the U.S. HPI data is illustrated for the proposed methodology.
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U2 - 10.1080/01621459.2013.772897
DO - 10.1080/01621459.2013.772897
M3 - Article
AN - SCOPUS:84890055402
SN - 0162-1459
VL - 108
SP - 929
EP - 941
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 503
ER -