A note on EM algorithm for mixture models
Expectation–maximization (EM) algorithm has been used to maximize the likelihood function or posterior when the model contains unobserved latent variables. One main important application of EM algorithm is to find the maximum likelihood estimator for mixture models. In this article, we propose an EM type algorithm to maximize a class of mixture type objective functions. In addition, we prove the monotone ascending property of the proposed algorithm and discuss some of its applications.
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Volume (Year): 83 (2013)
Issue (Month): 2 ()
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- Linton, Oliver & Xiao, Zhijie, 2007.
"A Nonparametric Regression Estimator That Adapts To Error Distribution Of Unknown Form,"
Cambridge University Press, vol. 23(03), pages 371-413, June.
- Linton, Oliver Bruce & Xiao, Zhijie, 2001. "A nonparametric regression estimator that adapts to error distribution of unknown form," SFB 373 Discussion Papers 2001,33, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
- Oliver Linton & Zhijie Xiao, 2001. "A Nonparametric Regression Estimator that Adapts to Error Distribution of Unknown Form," STICERD - Econometrics Paper Series 419, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
- Oliver Linton & Zhijie Xiao, 2001. "A nonparametric regression estimator that adapts to error distribution of unknown form," LSE Research Online Documents on Economics 2120, London School of Economics and Political Science, LSE Library.
- Yingcun Xia, 2004. "Efficient estimation for semivarying-coefficient models," Biometrika, Biometrika Trust, vol. 91(3), pages 661-681, September.
- Ao Yuan & Jan G. De Gooijer, 2007. "Semiparametric Regression with Kernel Error Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(4), pages 841-869.
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