Browsing Statistics by Subject "EM algorithm"

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Browsing Statistics by Subject "EM algorithm"

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  • Huang, Mian; Li, Runze; Wang, Hansheng; Yao, Weixin (2014)
    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 ...
  • Yao, Weixin; Zhao, Zhibiao (2013)
    For linear regression models with non normally distributed errors, the least squares estimate (LSE) will lose some efficiency compared to the maximum likelihood estimate (MLE). In this article, we propose a kernel density-based ...
  • Huang, Mian; Yao, Weixin (2012)
    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 ...
  • Yao, Weixin (2013)
    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 ...
  • Yao, Weixin (2010)
    It is well known that the normal mixture with unequal variance has unbounded likelihood and thus the corresponding global maximum likelihood estimator (MLE) is undefined. One of the commonly used solutions is to put a ...
  • Bai, Xiuqin; Yao, Weixin; Boyer, John E. (2012)
    The existing methods for tting mixture regression models assume a normal dis- tribution for error and then estimate the regression parameters by the maximum likelihood estimate (MLE). In this article, we demonstrate ...
  • Song, Weixing; Yao, Weixin; Xing, Yanru (2014)
    A robust estimation procedure for mixture linear regression models is proposed by assuming that the error terms follow a Laplace distribution. Using the fact that the Laplace distribution can be written as a scale mixture ...
  • Yao, Weixin; Wei, Yan; Yu, Chun (2014)
    The traditional estimation of mixture regression models is based on the normal assumption of component errors and thus is sensitive to outliers or heavy-tailed errors. A robust mixture regression model based on the ...
  • Cao, J.; Yao, Weixin (2012)
    Many historical datasets contain a large number of zeros, and cannot be modeled directly using a single distribution. Motivated by rain data from a global climate model, we study a semiparametric mixture of binomial ...