Reparameterizing the generalized linear model to accelerate gibbs sampler convergence

Albert and Chib introduced a complete Bayesian method to analyze data arising from the generalized linear model in which they used the Gibbs sampling algorithm facilitated by latent variables. Recently, Cowles proposed an alternative algorithm to accelerate the convergence of the Albert-Chib algorithm. The novelty in this latter algorithm is achieved by using a Hastings algorithm to generate latent variables and bin boundary parameters jointly instead of individually from their respective full conditionals. In the same spirit, we reparameterize the cumulative-link generalized linear model to accelerate the convergence of Cowles’ algorithm even further. One important advantage of our method is that for the three-bin problem it does not require the Hastings algorithm. In addition, for problems with more than three bins, while the Hastings algorithm is required, we provide a proposal density based on the Dirichlet distribution which is more natural than the truncated normal density used in the competing algorithm. Also, using diagnostic procedures recommended in the literature for the Markov chain Monte Carlo algorithm (both single and multiple runs) we show that our algorithm is substantially better than the one recently obtained. Precisely, our algorithm provides faster convergence and smaller autocorrelations between the iterates. Using the probit link function, extensive results are obtained for the three-bin and the five-bin multinomial ordinal data problems.     

Selection of a stroke risk model based on transcranial Doppler ultrasound velocity

Increased transcranial Doppler ultrasound (TCD) velocity is an indicator of cerebral infarction in children with sickle cell disease (SCD). In this article, the parallel genetic algorithm (PGA) is used to select a stroke risk model with TCD velocity as the response variable. Development of such a stroke risk model leads to the identification of children with SCD who are at a higher risk of stroke and their treatment in the early stages. Using blood velocity data from SCD patients, it is shown that the PGA is an easy-to-use computationally variable selection tool. The results of the PGA are also compared with those obtained from the stochastic search variable selection method, the Dantzig selector and conventional techniques such as stepwise selection and best subset selection.     

Variable selection in joint mean and variance models of Box–Cox transformation

In many applications, a single Box–Cox transformation cannot necessarily produce the normality, constancy of variance and linearity of systematic effects. In this paper, by establishing a heterogeneous linear regression model for the Box–Cox transformed response, we propose a hybrid strategy, in which variable selection is employed to reduce the dimension of the explanatory variables in joint mean and variance models, and Box–Cox transformation is made to remedy the response. We propose a unified procedure which can simultaneously select significant variables in the joint mean and variance models of Box–Cox transformation which provide a useful extension of the ordinary normal linear regression models. With appropriate choice of the tuning parameters, we establish the consistency of this procedure and the oracle property of the obtained estimators. Moreover, we also consider the maximum profile likelihood estimator of the Box–Cox transformation parameter. Simulation studies and a real example are used to illustrate the application of the proposed methods.     

Validating protein structure using kernel density estimates

Measuring the quality of determined protein structures is a very important problem in bioinformatics. Kernel density estimation is a well-known nonparametric method which is often used for exploratory data analysis. Recent advances, which have extended previous linear methods to multi-dimensional circular data, give a sound basis for the analysis of conformational angles of protein backbones, which lie on the torus. By using an energy test, which is based on interpoint distances, we initially investigate the dependence of the angles on the amino acid type. Then, by computing tail probabilities which are based on amino-acid conditional density estimates, a method is proposed which permits inference on a test set of data. This can be used, for example, to validate protein structures, choose between possible protein predictions and highlight unusual residue angles.     

Longitudinal data: different approaches in the context of item-response theory models

In this paper, some extended Rasch models are analyzed in the presence of longitudinal measurements of a latent variable. Two main approaches, multidimensional and multilevel, are compared: we investigate the different information that can be obtained from the latent variable, and we give advice on the use of the different kinds of models. The multidimensional and multilevel approaches are illustrated with a simulation study and with a longitudinal study on the health-related quality of life in terminal cancer patients.     

ESTIMATING BAYESIAN NETWORKS FOR HIGH‐DIMENSIONAL DATA WITH COMPLEX MEAN STRUCTURE AND RANDOM EFFECTS

The estimation of Bayesian networks given high‐dimensional data, in particular gene expression data, has been the focus of much recent research. Whilst there are several methods available for the estimation of such networks, these typically assume that the data consist of independent and identically distributed samples. It is often the case, however, that the available data have a more complex mean structure, plus additional components of variance, which must then be accounted for in the estimation of a Bayesian network. In this paper, score metrics that take account of such complexities are proposed for use in conjunction with score‐based methods for the estimation of Bayesian networks. We propose first, a fully Bayesian score metric, and second, a metric inspired by the notion of restricted maximum likelihood. We demonstrate the performance of these new metrics for the estimation of Bayesian networks using simulated data with known complex mean structures. We then present the analysis of expression levels of grape‐berry genes adjusting for exogenous variables believed to affect the expression levels of the genes. Demonstrable biological effects can be inferred from the estimated conditional independence relationships and correlations amongst the grape‐berry genes.     

基于中国上市公司的政府干预、金融市场化与法人股价值研究

以上市公司2000～2004年法人股协议转让的经验数据为样本,实证分析了政府干预和金融市场化程度对法人股价值的影响.研究发现,政府干预会降低法人股协议转让价格,但这种影响主要体现在国有企业上,对民营企业的影响并不显著.这为政府掠夺之手的理论提供了新的证据,同时也能更深入地了解政府干预对不同所有制企业的影响.此外,还发现发达的外部金融市场可以提高法人股转让价格,这表明金融中介体系作为产权保护的替代机制,能够通过约束经理人行为以及提高企业效率为企业带来治理溢价.     

A novel portfolio selection model in a hybrid uncertain environment

The future returns of each securities cannot be correctly reflected by the securities data in the past, therefore the statistical techniques and the experts’ judgement and experience are combined together to estimate the security returns in the future. In this paper, the returns of each securities are assumed to be fuzzy random variables, then following the ideas of mean variance model a new portfolio selection model in a hybrid uncertain environment is proposed. Moreover, the λ-mean variance efficient frontiers and λ-mean variance efficient portfolios are defined, and the properties of λ-mean variance efficient portfolios located on different λ-mean variance efficient frontiers are discussed. Finally, a numerical example is presented to illustrate the proposed portfolio selection model. On the basis of the results, we can conclude that the proposed model can provide the more flexible results.     

认知理论指导下的语言迁移研究

与以前的语言迁移研究相比,认知理论指导下的语言迁移研究更注重学习者作为认知主体的作用.本文从制约迁移的因素、可迁移性分析和作为策略的语言迁移几方面分析了这一突出特点.     

Two simple measures of variability for categorical data

This paper proposes two new variability measures for categorical data. The first variability measure is obtained as one minus the square root of the sum of the squares of the relative frequencies of the different categories. The second measure is obtained by standardizing the first measure. The measures proposed are functions of the variability measure proposed by Gini [Variabilitá e Mutuabilitá Contributo allo Studio delle Distribuzioni e delle Relazioni Statistiche, C. Cuppini, Bologna, 1912] and approximate the coefficient of nominal variation introduced by Kvålseth [Coefficients of variation for nominal and ordinal categorical data, Percept. Motor Skills 80 (1995), pp. 843–847] when the number of categories increases. Different mathematical properties of the proposed variability measures are studied and analyzed. Several examples illustrate how the variability measures can be interpreted and used in practice.