Mixture of regression models with varying mixing proportions: a semiparametric approach

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dc.contributor.author Huang, Mian
dc.contributor.author Yao, Weixin
dc.date.accessioned 2012-09-13T15:06:12Z
dc.date.available 2012-09-13T15:06:12Z
dc.date.issued 2012-09-13
dc.identifier.uri http://hdl.handle.net/2097/14702
dc.description.abstract In this article, we study a class of semiparametric mixtures of regression models, in which the regression functions are linear functions of the predictors, but the mixing proportions are smoothing functions of a covariate.We propose a one-step backfitting estimation procedure to achieve the optimal convergence rates for both regression parameters and the nonparametric functions of mixing proportions.We derive the asymptotic bias and variance of the one-step estimate, and further establish its asymptotic normality. A modified expectation-maximizationtype (EM-type) estimation procedure is investigated. We show that the modified EM algorithms preserve the asymptotic ascent property. Numerical simulations are conducted to examine the finite sample performance of the estimation procedures. The proposed methodology is further illustrated via an analysis of a real dataset. en_US
dc.relation.uri http://www.tandfonline.com/doi/full/10.1080/01621459.2012.682541 en_US
dc.rights This is an electronic version of an article published in Huang, M., & Yao, W. (2012). Mixture of regression models with varying mixing proportions: A semiparametric approach. Journal of the American Statistical Association, 107(498), 711-724. Journal of the American Statistical Association is available online at: http://www.tandfonline.com/ . en_US
dc.subject EM algorithm en_US
dc.subject Kernel regression en_US
dc.subject Mixture of regression models en_US
dc.subject Nonparametric regression en_US
dc.subject Semiparametric model en_US
dc.title Mixture of regression models with varying mixing proportions: a semiparametric approach en_US
dc.type Article (author version) en_US
dc.date.published 2012 en_US
dc.citation.doi doi:10.1080/01621459.2012.682541 en_US
dc.citation.epage 724 en_US
dc.citation.issue 498 en_US
dc.citation.jtitle Journal of the American Statistical Association en_US
dc.citation.spage 711 en_US
dc.citation.volume 107 en_US
dc.contributor.authoreid wxyao en_US

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