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Browsing Statistics by Author "Yao, Weixin"

Wang, Qin; Yao, Weixin
(2012)
Minimum average variance estimation (MAVE, Xia et al: 2002) is an effective dimension reduction method. It requires no strong probabilistic assumptions on the predictors, and can consistently estimate the central mean ...

Yao, Weixin
(2012)
Label switching is one of the fundamental issues for Bayesian mixture modeling. It
occurs due to the nonidentifiability of the components under symmetric priors. Without
solving the label switching, the ergodic averages ...

Yao, Weixin; Lindsay, Bruce G.
(2009)
A fundamental problem for Bayesian mixture model analysis is label switching, which
occurs due to the nonidentifiability of the mixture components under symmetric priors.
We propose two labelling methods to solve this ...

Yao, Weixin
(2012)
In this article, we propose a new method of bias reduction in nonparametric regression estimation. The proposed new estimator has asymptotic bias order h4, where h is a smoothing parameter, in contrast to the the usual ...

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 densitybased ...

Song, Weixing; Yao, Weixin
(2011)
The problem of fitting a parametric model in Tobit errorsinvariables 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 ...

Xiang, Sijia; Yao, Weixin; Wu, Jingjing
(2014)
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)
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 mcomponent mixture model is a ...

Yao, Weixin
(2013)
Expectationmaximization (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 ...

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 heavytailed 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 modelfree 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 ...