Article history: Received 17 May 2015 Received in revised form 19 July 2015 Accepted 22 July 2015 Available online 30 July 2015

In this study, a new three-statement randomized response estimation method is proposed to improve the drawback that maximum likelihood could generate negative value estimate sensitive-nature proportion (SNP) when its true small. The Bayes estimator of SNP obtained via using hierarchical Bayesian modeling procedure. Moreover, hybrid algorithm Gibbs sampling in Metropolis–Hastings algorithms used...

The problem discussed here has arisen from an industrial scenario involving the potential failure of an element of building structures. The element carries with it a warranty of several years. The scenario considered is not specific to buildings and occurs under other circumstances; it goes under the label “product stewardship.” Its statistical content, however, is inference from accelerated te...

In recent years, sparse classification problems have emerged in many fields of study. Finite mixture models have been developed to facilitate Bayesian inference where parameter sparsity is substantial. Classification with finite mixture models is based on the posterior expectation of latent indicator variables. These quantities are typically estimated using the expectation-maximization (EM) alg...

Hierarchical text classification or simply hierarchical classification refers to assigning a document to one or more suitable categories from a hierarchical category space. In our literature survey, we have found that the existing hierarchical classification experiments used a variety of measures to evaluate performance. These performance measures often assume independence between categories an...

We propose an empirical Bayes method for variable selection and coefficient estimation in linear regression models. The method is based on a particular hierarchical Bayes formulation, and the empirical Bayes estimator is shown to be closely related to the LASSO estimator. Such a connection allows us to take advantage of the recently developed quick LASSO algorithm to compute the empirical Bayes...

We present a new PAC-Bayes generalization bound for structured prediction that is applicable to perturbation-based probabilistic models. Our analysis explores the relationship between perturbation-based modeling and the PAC-Bayes framework, and connects to recently introduced generalization bounds for structured prediction. We obtain the first PAC-Bayes bounds that guarantee better generalizati...

We develop a quantum probability model that can account for situations where people’s causal judgments violate the properties of causal Bayes nets and demonstrate how the parameters of our model can be interpreted to provide information about underlying cognitive processes. We implement this model within a hierarchical Bayesian inference framework that allows us to systematically identify indiv...

For the problem of variable selection in generalized linear models, we develop various adaptive Bayesian criteria. Using a hierarchical mixture setup for model uncertainty, combined with an integrated Laplace approximation, we derive Empirical Bayes and Fully Bayes criteria that can be computed easily and quickly. The performance of these criteria is assessed via simulation and compared to othe...

For the problem of variable selection in generalized linear models, we develop various adaptive Bayesian criteria. Using a hierarchical mixture setup for model uncertainty, combined with an integrated Laplace approximation, we derive Empirical Bayes and Fully Bayes criteria that can be computed easily and quickly. The performance of these criteria is assessed via simulation and compared to othe...