Estimating Mixture of Gaussian Processes by Kernel Smoothing

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Show simple item record Huang, Mian Li, Runze Wang, Hansheng Yao, Weixin 2014-12-03T16:54:30Z 2014-12-03T16:54:30Z 2014-12-03
dc.description.abstract When functional data are not homogenous, for example, when there are multiple classes of functional curves in the dataset, traditional estimation methods may fail. In this article, we propose a new estimation procedure for the mixture of Gaussian processes, to incorporate both functional and inhomogenous properties of the data. Our method can be viewed as a natural extension of high-dimensional normal mixtures. However, the key difference is that smoothed structures are imposed for both the mean and covariance functions. The model is shown to be identifiable, and can be estimated efficiently by a combination of the ideas from expectation-maximization (EM) algorithm, kernel regression, and functional principal component analysis. Our methodology is empirically justified by Monte Carlo simulations and illustrated by an analysis of a supermarket dataset. en_US
dc.language.iso en_US en_US
dc.relation.uri en_US
dc.rights This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Business & Economic Statistics on 2014, available online: en_US
dc.subject Identi ability en_US
dc.subject EM algorithm en_US
dc.subject Kernel regression en_US
dc.subject Gaussian process en_US
dc.subject Functional principal component analysis en_US
dc.title Estimating Mixture of Gaussian Processes by Kernel Smoothing en_US
dc.type Article (author version) en_US 2014 en_US
dc.citation.doi 10.1080/07350015.2013.868084 en_US
dc.citation.epage 270 en_US
dc.citation.issue 2 en_US
dc.citation.jtitle Journal of Business & Economic Statistics en_US
dc.citation.spage 259 en_US
dc.citation.volume 32 en_US
dc.contributor.authoreid wxyao en_US

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