Nonparametric Bayesian inference for multivariate density functions using Feller priors

Multivariate density estimation plays an important role in investigating the mechanism of high-dimensional data. This article describes a nonparametric Bayesian approach to the estimation of multivariate densities. A general procedure is proposed for constructing Feller priors for multivariate densities and their theoretical properties as nonparametric priors are established. A blocked Gibbs sampling algorithm is devised to sample from the posterior of the multivariate density. A simulation study is conducted to evaluate the performance of the procedure.     

A Method to Handle Zero Counts in the Multinomial Model

In the context of an objective Bayesian approach to the multinomial model, Dirichlet(a, …, a) priors with a < 1 have previously been shown to be inadequate in the presence of zero counts, suggesting that the uniform prior (a = 1) is the preferred candidate. In the presence of many zero counts, however, this prior may not be satisfactory either. A model selection approach is proposed, allowing for the possibility of zero parameters corresponding to zero count categories. This approach results in a posterior mixture of Dirichlet distributions and marginal mixtures of beta distributions, which seem to avoid the problems that potentially result from the various proposed Dirichlet priors, in particular in the context of extreme data with zero counts.     

Bayesian bridge-randomized penalized quantile regression estimation for linear regression model with AP(q) perturbation

Bridge penalized regression has many desirable statistical properties such as unbiasedness, sparseness as well as ‘oracle’. In Bayesian framework, bridge regularized penalty can be implemented based on generalized Gaussian distribution (GGD) prior. In this paper, we incorporate Bayesian bridge-randomized penalty and its adaptive version into the quantile regression (QR) models with autoregressive perturbations to conduct Bayesian penalization estimation. Employing the working likelihood of the asymmetric Laplace distribution (ALD) perturbations, the Bayesian joint hierarchical models are established. Based on the mixture representations of the ALD and generalized Gaussian distribution (GGD) priors of coefficients, the hybrid algorithms based on Gibbs sampler and Metropolis-Hasting sampler are provided to conduct fully Bayesian posterior estimation. Finally, the proposed Bayesian procedures are illustrated by some simulation examples and applied to a real data application of the electricity consumption.     

Bayesian approach to epsilon-skew-normal family

The estimation problem of epsilon-skew-normal (ESN) distribution parameters is considered within Bayesian approaches. This family of distributions contains the normal distribution, can be used for analyzing the asymmetric and near-normal data. Bayesian estimates under informative and non informative Jeffreys prior distributions are obtained and performances of ESN family and these estimates are shown via a simulation study. A real data set is also used to illustrate the ideas.     

Letter to the Editor: Respecting the Plurality of Value and the Messiness of Scientific Practice

Evaluations of authorship and recommendations for authorship policies best heed the plurality of valuation cultures that traverse scientific practices and respect the messiness of scientific practices, for those are reflected in authorship and authorship sequence decisions.     

Bayesian analysis of a multiple-recapture model

In the Bayesian analysis of a multiple-recapture census, different diffuse prior distributions can lead to markedly different inferences about the population size N. Through consideration of the Fisher information matrix it is shown that the number of captures in each sample typically provides little information about N. This suggests that if there is no prior information about capture probabilities, then knowledge of just the sample sizes and not the number of recaptures should leave the distribution of Nunchanged. A prior model that has this property is identified and the posterior distribution is examined. In particular, asymptotic estimates of the posterior mean and variance are derived. Differences between Bayesian and classical point and interval estimators are illustrated through examples.     

Collaborative Data-Mining in an Adolescent Mental Health Service

Summary

As full participants in a collaborative clinical data-mining project intended to promote staff reflection and to improve services, clinicians discovered just how valuable their practice expertise was in maintaining project relevance. In this paper, they describe challenges they faced as non-experienced researchers and writers and how their practice skills enabled them to overcome these challenges. Benefits derived include: enhanced skills (in both practice and research), sensitization to previously overlooked areas of practice, and an increased sense of professionalism.     

当代我国出版自由研究

当代我国的＂出版自由＂,应着眼于法律如何保障出版自由和如何规制出版自由。即现阶段我国＂出版自由＂的内涵是：出版自由是公民在宪法、法律规定的范围内,将作品编辑、加工并复制后向公众传播,以出版的形式发表意见来参与管理国家、公共等事务的权利。研究＂出版自由＂时应正确区分出版自由和政治自由、表达自由、新闻自由、议论自由的区别与联系。我国现行的法律法规有明确的《中华人民共和国著作权法》,却没有＂出版自由法＂,其实＂出版自由权＂与＂著作权＂有着至关重要的关系,出版自由权是著作权的前提和保证,而著作权则是出版自由权得以落实和具体化的司法手段。     

Ranking of the Most Prolific Authors,Universities, and Organizations in the Journal of Organizational Behavior Management

ABSTRACT

Over the years, reviews in behavior analysis have sought to identify the most prolific researchers and institutions. The goal of these reviews was to offer one resource for behavior analysts to identify experts in behavior analysis and quality graduate programs. However, most of these reviews omitted the Journal of Organizational Behavior Management (JOBM), making the results less relevant to those who work in organizational behavior management. The purpose of this review was to extend previous findings and identify the most published researchers, academic institutions, and organizations in JOBM since its inception. Furthermore, we calculated the citation rate (per year, per article) for each of the top 20 most published authors. The results, implications, and opportunities for further analysis are discussed.     

Addressing potential prior‐data conflict when using informative priors in proof‐of‐concept studies

Bayesian methods are increasingly used in proof‐of‐concept studies. An important benefit of these methods is the potential to use informative priors, thereby reducing sample size. This is particularly relevant for treatment arms where there is a substantial amount of historical information such as placebo and active comparators. One issue with using an informative prior is the possibility of a mismatch between the informative prior and the observed data, referred to as prior‐data conflict. We focus on two methods for dealing with this: a testing approach and a mixture prior approach. The testing approach assesses prior‐data conflict by comparing the observed data to the prior predictive distribution and resorting to a non‐informative prior if prior‐data conflict is declared. The mixture prior approach uses a prior with a precise and diffuse component. We assess these approaches for the normal case via simulation and show they have some attractive features as compared with the standard one‐component informative prior. For example, when the discrepancy between the prior and the data is sufficiently marked, and intuitively, one feels less certain about the results, both the testing and mixture approaches typically yield wider posterior‐credible intervals than when there is no discrepancy. In contrast, when there is no discrepancy, the results of these approaches are typically similar to the standard approach. Whilst for any specific study, the operating characteristics of any selected approach should be assessed and agreed at the design stage; we believe these two approaches are each worthy of consideration. Copyright © 2015 John Wiley & Sons, Ltd.