Semiparametric Bayesian classification with longitudinal markers

Summary.  We analyse data from a study involving 173 pregnant women. The data are observed values of the β human chorionic gonadotropin hormone measured during the first 80 days of gestational age, including from one up to six longitudinal responses for each woman. The main objective in this study is to predict normal versus abnormal pregnancy outcomes from data that are available at the early stages of pregnancy. We achieve the desired classification with a semiparametric hierarchical model. Specifically, we consider a Dirichlet process mixture prior for the distribution of the random effects in each group. The unknown random-effects distributions are allowed to vary across groups but are made dependent by using a design vector to select different features of a single underlying random probability measure. The resulting model is an extension of the dependent Dirichlet process model, with an additional probability model for group classification. The model is shown to perform better than an alternative model which is based on independent Dirichlet processes for the groups. Relevant posterior distributions are summarized by using Markov chain Monte Carlo methods.     

Markov bridges and enlarged filtrations

The semimartingale decomposition of a Markov chain, whose value at some future time is known, is obtained by considering an enlarged filtration.     

Private Labels: Facilitators or Impediments to Supply Chain Coordination

We consider a retailer’s decision of whether to develop an internally produced, private label version of a national brand and the role that this decision plays in coordinating the supply chain. Our model assumes that the perceived quality of the private label is lower than that of the national brand, and we allow for the two products to have different marginal costs. We further allow for a fixed development cost that the retailer must incur to develop private label capability, and distinguish two types of private labels depending upon whether they would or would not be developed as product line extensions by a vertically integrated supply chain. We refer to these two types as first‐best (FB) and non‐first‐best (NFB) product line extensions, respectively. When the private label can be characterized as a NFB product line extension, its development creates adverse cannibalization effects, yet it also helps to mitigate the effects of double marginalization with respect to the national brand. We characterize the conditions under which the retailer will develop private label capability, and distinguish among the conditions under which this is either beneficial or detrimental to the overall performance of the supply chain.     

人口老龄化对企业出口国内增加值率的影响研究

中国经济正面临出口国内增加值率偏低和人口老龄化的双向挑战。基于2000~2013年中国微观企业数据,文章研究了人口老龄化对企业出口国内增加值率的影响。结果显示,人口老龄化会负向影响中国企业出口国内增加值率,且在克服内生性问题、进行多种稳健性检验后,这一结论依然成立。同时,这种负向影响主要是由进口中间品对国内中间品及劳动力的替代所导致的,而人口老龄化的成本效应对出口国内增加值率的不利影响相对较小,并且从贸易结构变动来看,人口老龄化并未通过降低企业从事加工贸易的比重,进而获得总体企业平均出口国内增加值率的“被动”提升。进一步研究还表明上述负向影响在混合贸易企业、劳动密集型企业、外资企业与人口净流出地区企业上体现得更为明显。     

人才供应链培养模式下高职个人投资、收益与风险防范研究

在人才供应链下培养模式下,作为“原材料”的高中生越来越重视高职教育的投资与收益问题,本文通过分析高职教育个人投资与收益,从个人、高职院校、企业和政府四个方面提出了防范高职教育的个人投资风险。     

Reversible jump Markov chain Monte Carlo algorithms for Bayesian variable selection in logistic mixed models

In this article, to reduce computational load in performing Bayesian variable selection, we used a variant of reversible jump Markov chain Monte Carlo methods, and the Holmes and Held (HH) algorithm, to sample model index variables in logistic mixed models involving a large number of explanatory variables. Furthermore, we proposed a simple proposal distribution for model index variables, and used a simulation study and real example to compare the performance of the HH algorithm with our proposed and existing proposal distributions. The results show that the HH algorithm with our proposed proposal distribution is a computationally efficient and reliable selection method.     

Application of a non-homogeneous Markov chain with seasonal transition probabilities to ozone data

In this work, we assume that the sequence recording whether or not an ozone exceedance of an environmental threshold has occurred in a given day is ruled by a non-homogeneous Markov chain of order one. In order to account for the possible presence of cycles in the empirical transition probabilities, a parametric form incorporating seasonal components is considered. Results show that even though some covariates (namely, relative humidity and temperature) are not included explicitly in the model, their influence is captured in the behavior of the transition probabilities. Parameters are estimated using the Bayesian point of view via Markov chain Monte Carlo algorithms. The model is applied to ozone data obtained from the monitoring network of Mexico City, Mexico. An analysis of how the methodology could be used as an aid in the decision-making is also given.     

Independent attributes control chart for Markov-dependent processes

Statistical process monitoring (SPM) is a very efficient tool to maintain and to improve the quality of a product. In many industrial processes, end product has two or more attribute-type quality characteristics. Some of them are independent, but the observations are Markovian dependent. It is essential to develop a control chart for such situations. In this article, we develop an Independent Attributes Control Chart for Markov Dependent Processes based on error probabilities criterion under the assumption of one-step Markov dependency. Implementation of the chart is similar to that of Shewhart-type chart. Performance of the chart has been studied using probability of detecting shift criterion. A procedure to identify the attribute(s) responsible for out-of-control status of the process is given.     

R package rjmcmc: reversible jump MCMC using post‐processing

The rjmcmc package for R implements the post‐processing reversible jump Markov chain Monte Carlo (MCMC) algorithm of Barker & Link. MCMC output from each of the models is used to estimate posterior model probabilities and Bayes factors. Automatic differentiation is used to simplify implementation. The package is demonstrated on two examples.     

Migration pattern in Bangladesh: a covariate-dependent Markov model

Internal migration is one of the major components of rapid and unplanned growth of towns and cities especially in the developing countries. This paper describes the transition pattern of internal out migration in Bangladesh and some sociodemographic factors influencing such migration in the country using a covariate-dependent Markov model. Four types of migration behavior namely, rural to rural, rural to urban, urban to rural and urban to urban are under consideration of this paper. Defining two discrete states, urban and rural, each of such transition can be characterized by a stochastic process; hence we use a two-state Markov chain for this purpose. We find that age, sex, division and reason of migration are significantly associated with internal migration in Bangladesh. The major findings include that any type of migration, rural to rural, rural to urban, urban to rural and urban to urban, mostly take place at the ages of 15–30 as well as at the ages of 0–15; females have higher odds than males to make a migration; Dhaka, Rajshahi and Chittagong divisions have remarkably higher migration rate as compared to Barisal and Sylhet division; and the professional reason is the main reason for rural to urban migration.