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  • Zhang, Hongyan; Wang, Haiyan; Dai, Zhijun; Chen, Ming-Shun; Yuan, Zheming (2012)
    Background: Even though the classification of cancer tissue samples based on gene expression data has advanced considerably in recent years, it faces great challenges to improve accuracy. One of the challenges is to establish ...
  • 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 ...
  • Song, Weixing; Yao, Weixin (2011)
    The problem of fitting a parametric model in Tobit errors-in-variables regression models is discussed in this paper. The proposed test is based on the supremum of the Khamaladze type transformation of a certain partial ...
  • Yao, Weixin; Lindsay, Bruce G.; Li, Runze (2012)
    A local modal estimation procedure is proposed for the regression function in a nonparametric regression model. A distinguishing characteristic of the proposed procedure is that it introduces an additional tuning parameter ...
  • 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 (2012)
    Label switching is one of the fundamental problems for Bayesian mixture model analysis. Due to the permutation invariance of the mixture posterior, we can consider that the posterior of a m-component mixture model is a ...
  • Wang, Haiyan; Maldonado, Diego; Silwal, Sharad (2011)
    In image processing, image similarity indices evaluate how much structural information is maintained by a processed image in relation to a reference image. Commonly used measures,such as the mean squared error (MSE) and ...
  • 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; Li, Longhai (2014)
    Solving label switching is crucial for interpreting the results of fitting Bayesian mixture models. The label switching originates from the invariance of posterior distribution to permutation of component labels. As a ...
  • 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 ...
  • Sundar, Raghav Prashant; Becker, Mark W.; Bello, Nora M.; Bix, Laura (2012)
    Adverse drug events (ADEs) are a significant problem in health care. While effective warnings have the potential to reduce the prevalence of ADEs, little is known about how patients access and use prescription labeling. ...
  • 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 ...
  • Yao, Weixin; Wang, Qin (2013)
    Dimension reduction and variable selection play important roles in high dimensional data analysis. The sparse MAVE, a model-free variable selection method, is a nice combination of shrinkage estimation, Lasso, and an ...
  • 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 ...
  • Anderson, Dean M.; Murray, Leigh W. (2013)
    Turning preferences among 309 white-faced ewes were individually evaluated in an enclosed, artificially lighted, T-maze, followed by each ewe choosing either a right or left return alley to return to peers. Data recorded ...
  • Wang, Haiyan; Zhang, Hongyan; Dai, Zhijun; Chen, Ming-Shun; Yuan, Zheming (2013)
    Background: One of the challenges in classification of cancer tissue samples based on gene expression data is to establish an effective method that can select a parsimonious set of informative genes. The Top Scoring Pair ...