TANG NianshengKey Lab of Statistical Modeling and Data Analysis of Yunnan Province系统科学与复杂性学报(英文版)Zhang Y Q and Tang N S, Bayesian empirical likelihood estimation of quantile structural equation models, Journal of Systems Science & Complexity, 2017, 30(1): 122-138....
.ThispaperconsiderstheBayesianempiricallikelihoodapproachtoquantileregression.Tak-ingtheempiricallikelihoodintoaBayesianframework,weshowthattheresultantposteriorfromanyfixedpriorisasymptoticallynormal;itsmeanshrinkstowardsthetrueparametervaluesanditsvarianceapproachesthatofthemaximumempiricallikelihoodestimator.Amoreinteresting...
This paper considers the Bayesian empirical likelihood approach to quantile regression. Taking the empirical likelihood into a Bayesian framework, we show that the resultant posterior from any fixed prior is asymptotically normal; its mean shrinks toward the true parameter values, and its variance ...
For complex survey data, we show that a pseudo-empirical-likelihood approach can be used to construct Bayesian pseudo-empirical-likelihood intervals that are asymptotically valid under the design-based set-up. The approach proposed compares favourably with a full Bayesian analysis under simple random ...
1) For every gene, obtain an empirical theta using the maximum likelihood model (theta.ml function of the MASS R package) and the estimated mean vector that is obtained by a generalized linear model with Poisson error distribution. 2) Fit a line (loess, span=0.33, degree=2) through the ...
In this article, we develop a Bayesian semiparametric analysis of moment condition models by casting the problem within the exponentially tilted empirical likelihood (ETEL) framework. We use this framework to develop a fully Bayesian analysis of correctly and misspecified moment condition models. We ...
EvoRadicalimplements the models of[9], which use partitions of amino acids to parameterize the rates of property-conserving and property-altering codon substitutions in a maximum likelihood framework. The model considers three types of substitutions: synonymous, property-conserving nonsynonymous and propert...
Granger causality does not rely on the specification of a scientific model and thus is particularly applicable to investigation of empirical cause-effect relationships. It is noted that Granger causality is especially suited for continuous-valued data based on frequentist hypothesis testing. The goal of...
The first building block of a parametric Bayesian model is the likelihood The likelihood is equal to theprobability densityof when the parameter of the data generating distribution is equal to . For the time being, we assume that and arecontinuous. Later, we will discuss how to relax this ass...
Empirical Bayesian kriging has a number of advantages and disadvantages compared to other interpolation methods. Advantages Requires minimal interactive modeling. Standard errors of prediction are more accurate than other kriging methods. Allows accurate predictions of moderately nonstationary data...