A non-homogeneous hidden Markov model for precipitation occurrence

A non-homogeneous hidden Markov model is proposed for relating precipitation occurrences at multiple rain-gauge stations to broad scale atmospheric circulation patterns (the so-called 'downscaling problem'). We model a 15-year sequence of winter data from 30 rain stations in south-western Australia. The first 10 years of data are used for model development and the remaining 5 years are used for model evaluation. The fitted model accurately reproduces the observed rainfall statistics in the reserved data despite a shift in atmospheric circulation (and, consequently, rainfall) between the two periods. The fitted model also provides some useful insights into the processes driving rainfall in this region.     

On proportional hazards assumption under the random effects models

The proportional hazards mixed-effects model (PHMM) was proposed to handle dependent survival data. Motivated by its application in genetic epidemiology, we study the interpretation of its parameter estimates under violations of the proportional hazards assumption. The estimated fixed effect turns out to be an averaged regression effect over time, while the estimated variance component could be unaffected, inflated or attenuated depending on whether the random effect is on the baseline hazard, and whether the non-proportional regression effect decreases or increases over time. Using the conditional distribution of the covariates we define the standardized covariate residuals, which can be used to check the proportional hazards assumption. The model checking technique is illustrated on a multi-center lung cancer trial.     

基于改进GMDH和AC聚类算法的竞争力模型研究——钢铁行业竞争力分析

基于自组织数据挖掘算法,以钢铁行业为分析背景,以新的视角对竞争力模型开展研究。首先运用GMDH算法的自动筛选特性生成钢铁行业竞争力评价指标体系,并自动生成权重;然后通过AC聚类技术,结合GMDH算法所建立的指标体系,最终确立钢铁行业的竞争力分析模型。利用模型评价河北省钢铁行业的竞争地位,并与其它省市进行对比,认为河北是钢铁大省,并不是钢铁强省,要提高河北钢铁行业的竞争实力可以借鉴与其发展轨迹相似的上海和辽宁的发展经验。     

混合双参数指数分布的参数估计

混合指数分布是寿命数据分析中一个非常重要的分布。但是利用矩估计、极大似然估计等估计模型的参数往往比较困难。本文应用EM算法详细研究了混合双指数分布在正常工作条件下,在完全数据场合的参数估计问题。模拟说明利用EM算法来估计混合双指数分布是一种非常有效的方法,随着样本的增加,估计值离真值越来越近,估计值的方差越来越小。     

用复射线法分析凹槽散射

凹槽散射是开口腔结构的一种典型代表。用复射线方法十分简便地实现了凹槽结构散射宽度的预估，并用ＧＴＤ理论对复射线场在入射口面边缘上的绕射场贡献作了简化处理，计算结果与射线模式法所得结果吻合较好，但简化算法却可节省大量计算机时间。     

A bivariate gamma mixture distribution

In this paper we construct a bivariate gamma mixture distribution by allowing the scale parameters of the two marginals to have a generalized Bernoulli distribution. We study the statistical properties of this distribution and discuss the estimation of the parameters. The distribution is then fitted to two bivariate data sets. Data on the age and lactation period of cows are described well by the proposed model, but it fails to fit Johansen's bean data properly because of theoretical constraints on the correlation.     

Variable Selection for Naive Bayes Semisupervised Learning

This article deals with a semisupervised learning based on naive Bayes assumption. A univariate Gaussian mixture density is used for continuous input variables whereas a histogram type density is adopted for discrete input variables. The EM algorithm is used for the computation of maximum likelihood estimators of parameters in the model when we fix the number of mixing components for each continuous input variable. We carry out a model selection for choosing a parsimonious model among various fitted models based on an information criterion. A common density method is proposed for the selection of significant input variables. Simulated and real datasets are used to illustrate the performance of the proposed method.     

Practical Bayesian estimation of a finite beta mixture through gibbs sampling and its applications

This paper deals with a Bayesian analysis of a finite Beta mixture model. We present approximation method to evaluate the posterior distribution and Bayes estimators by Gibbs sampling, relying on the missing data structure of the mixture model. Experimental results concern contextual and non-contextual evaluations. The non-contextual evaluation is based on synthetic histograms, while the contextual one model the class-conditional densities of pattern-recognition data sets. The Beta mixture is also applied to estimate the parameters of SAR images histograms.     

Analysis of a long-term study of neurotic disorder, with insights into the process of non-response

Summary.  The paper studies the non-response process in a long-term study of neurotic dis-order by comparing the analysis based on the responses that were collected by the established practice of interviewing the subjects, at dates arranged in advance (appointments), with the analysis of the nearly complete set of responses that were collected by an extensive effort that involved attempts to interview without seeking a prior agreement. The method of multiple imputation is applied, and its properties are explored in a setting that is not perfectly suited for its application: a relatively small sample size, ordinal score outcomes and the likelihood that the outcomes are missing not at random.     

An Exponential-Family Multidimensional Scaling Mixture Methodology

A multidimensional scaling methodology (STUNMIX) for the analysis of subjects' preference/choice of stimuli that sets out to integrate the previous work in this area into a single framework, as well as to provide a variety of new options and models, is presented. Locations of the stimuli and the ideal points of derived segments of subjects on latent dimensions are estimated simultaneously. The methodology is formulated in the framework of the exponential family of distributions, whereby a wide range of different data types can be analyzed. Possible reparameterizations of stimulus coordinates by stimulus characteristics, as well as of probabilities of segment membership by subject background variables, are permitted. The models are estimated in a maximum likelihood framework. The performance of the models is demonstrated on synthetic data, and robustness is investigated. An empirical application is provided, concerning intentions to buy portable telephones.