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  • Yao, Weixin; Zhao, Zhibiao (2014-03-10)
    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 (2012-05-24)
    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 (2012-11-08)
    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 ...
  • Xiang, Sijia; Yao, Weixin; Wu, Jingjing (2014-12-03)
    In this paper, we propose a new effective estimator for a class of semiparametric mixture models where one component has known distribution with possibly unknown parameters while the other component density and the mixing ...
  • Huang, Mian; Yao, Weixin (2012-09-13)
    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 (2012-05-17)
    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 M.; Silwal, Sharad (2011-10-13)
    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 (2013-01-22)
    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 (2014-03-10)
    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 ...
  • Dai, Zhijun; Wang, Lifeng; Chen, Yuan; Wang, Haiyan; Bai, Lianyang; Yuan, Zheming (2014-06-24)
    In this paper, we present a pipeline to perform improved QSAR analysis of peptides. The modeling involves a double selection procedure that first performs feature selection and then conducts sample selection before the ...
  • Yao, Weixin (2012-05-25)
    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 (2012-08-01)
    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. (2012-06-18)
    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 (2014-03-10)
    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 (2014-03-10)
    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 (2013-05-22)
    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 ...
  • Trier, Tony; Bello, Nora M.; Bush, Tamara Reid; Bix, Laura (2014-10-01)
    Objective: The objective of this study was to assess the impact of package size on the contact between medical devices and non-sterile surfaces (i.e. the hands of the practitioner and the outside of the package) during ...
  • Cao, J.; Yao, Weixin (2012-05-24)
    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, Michael P.; Dubnicka, Suzanne R. (2015-03-04)
    DNA barcodes are short strands of 255–700 nucleotide bases taken from the cytochrome c oxidase subunit 1 (COI) region of the mitochondrial DNA. It has been proposed that these barcodes may be used as a method of differentiating ...
  • Anderson, Dean M.; Murray, Leigh W. (2013-05-02)
    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 ...

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