Analysis of Competing Risks by Using Bayesian Smoothing

We consider the competing risks set-up. In many practical situations, the conditional probability of the cause of failure given the failure time is of direct interest. We propose to model the competing risks by the overall hazard rate and the conditional probabilities rather than the cause-specific hazards. We adopt a Bayesian smoothing approach for both quantities of interest. Illustrations are given at the end.     

Hybridization and speciation in the carabid beetles of the subgenusOhomopterus (Coleoptera, Carabidae, genusCarabus)

Natural hybridization among wingless carabid beetles of the subgenusOhomopterus (Carabidae, genusCarabus) is reviewed, and its significance in the evolution of this subgenus discussed. Natural hybridization occurs between parapatric species of similar size. Two case studies of natural hybridization suggest that natural hybridization could have affected the evolution of this subgenus in different ways. When there is a large difference in genital morphology between hybridizing species, interspecific copulation often results in genital injuries that causes mortality of copulating individuals, and hence reduces the fitness of hybridizing individuals greatly. In such a case, hybridization may be effective in maintaining the parapatric distribution of the two species, and in the long term, may promote reinforcement selection for traits which are effective in prezygotic reproductive isolation. When the morphological difference in genitalia is not so large as to cause genital injury, a hybrid population may be established at the intermediate zone between two parental species, provided that the immigration rates of the two species into the intermediate zone are small. Thus, natural hybridization may have contributed to both divergence and reticulate evolution in this subgenus.     

考虑脉冲现象的复杂供应链系统及其稳定策略

供应链系统是一个复杂的系统,一方面由于时间延迟、信息不对称及反馈机制等,会产生牛鞭效应,另一方面供应链具有需求突增或骤减的脉冲特点,上述现象都可能导致供应链系统混沌,而现有的供应链模型仅考虑了“牛鞭效应”,并未考虑脉冲现象对供应链系统的影响。文章建立了考虑脉冲现象的供应链模型,分析了脉冲现象对供应链系统的影响,并给出了使供应链系统稳定的策略,讨论了参数扰动对该模型的影响,通过数值算例验证了稳定策略的有效性。研究发现:脉冲现象对供应链系统有着显著影响,但只要选取恰当的脉冲强度和脉冲时间间隔,供应链系统是可以稳定的。     

科学与技术关系的演进历史探究

科学与技术关系是科技史上一个重要问题,本文以科学与技术关系的历史演进为中心进行论述。立足科技史,首先对科学与技术的定义进行了概括,辨析了科学与技术的区别,然后着重阐述了科学与技术本质联系的演进历史阶段：平行发展时期、相互作用时期和融合创新时期。     

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.     

Adaptive Gaussian Markov random fields with applications in human brain mapping

Summary.  Functional magnetic resonance imaging has become a standard technology in human brain mapping. Analyses of the massive spatiotemporal functional magnetic resonance imaging data sets often focus on parametric or non-parametric modelling of the temporal component, whereas spatial smoothing is based on Gaussian kernels or random fields. A weakness of Gaussian spatial smoothing is underestimation of activation peaks or blurring of high curvature transitions between activated and non-activated regions of the brain. To improve spatial adaptivity, we introduce a class of inhomogeneous Markov random fields with stochastic interaction weights in a space-varying coefficient model. For given weights, the random field is conditionally Gaussian, but marginally it is non-Gaussian. Fully Bayesian inference, including estimation of weights and variance parameters, can be carried out through efficient Markov chain Monte Carlo simulation. Although motivated by the analysis of functional magnetic resonance imaging data, the methodological development is general and can also be used for spatial smoothing and regression analysis of areal data on irregular lattices. An application to stylized artificial data and to real functional magnetic resonance imaging data from a visual stimulation experiment demonstrates the performance of our approach in comparison with Gaussian and robustified non-Gaussian Markov random-field models.     

Bayesian analysis of human movement curves

Summary.  We consider the Bayesian analysis of human movement data, where the subjects perform various reaching tasks. A set of markers is placed on each subject and a system of cameras records the three-dimensional Cartesian co-ordinates of the markers during the reaching movement. It is of interest to describe the mean and variability of the curves that are traced by the markers during one reaching movement, and to identify any differences due to covariates. We propose a methodology based on a hierarchical Bayesian model for the curves. An important part of the method is to obtain identifiable features of the movement so that different curves can be compared after temporal warping. We consider four landmarks and a set of equally spaced pseudolandmarks are located in between. We demonstrate that the algorithm works well in locating the landmarks, and shape analysis techniques are used to describe the posterior distribution of the mean curve. A feature of this type of data is that some parts of the movement data may be missing—the Bayesian methodology is easily adapted to cope with this situation.     

Model-based clustering for social networks

Summary.  Network models are widely used to represent relations between interacting units or actors. Network data often exhibit transitivity, meaning that two actors that have ties to a third actor are more likely to be tied than actors that do not, homophily by attributes of the actors or dyads, and clustering. Interest often focuses on finding clusters of actors or ties, and the number of groups in the data is typically unknown. We propose a new model, the latent position cluster model , under which the probability of a tie between two actors depends on the distance between them in an unobserved Euclidean 'social space', and the actors' locations in the latent social space arise from a mixture of distributions, each corresponding to a cluster. We propose two estimation methods: a two-stage maximum likelihood method and a fully Bayesian method that uses Markov chain Monte Carlo sampling. The former is quicker and simpler, but the latter performs better. We also propose a Bayesian way of determining the number of clusters that are present by using approximate conditional Bayes factors. Our model represents transitivity, homophily by attributes and clustering simultaneously and does not require the number of clusters to be known. The model makes it easy to simulate realistic networks with clustering, which are potentially useful as inputs to models of more complex systems of which the network is part, such as epidemic models of infectious disease. We apply the model to two networks of social relations. A free software package in the R statistical language, latentnet, is available to analyse data by using the model.     

中国节节麦的细胞遗传学研究及有关问题探讨

应用染色体组分析法,对新疆节节麦、黄河流域节节麦、伊朗节节麦和中东节节麦的D染色体组进行了较系统的比较分析,结果表明:新疆节节麦与伊朗、中东两地节节麦在染色体组水平上无明显差异,其差异仅体现在染色体的细微结构组成上;臂内倒位是引起染色体结构组成上细微分化的主要原因。黄河流域节节麦与新疆节节麦的染色体组极为相近;其D组染色体无论在染色体组水平上,还是在染色体细微结构组成上均无差异。本文还就中国节节麦与中国特有六倍体普通小麦的起源演化,中国节节麦种质资源在现代小麦育种中的应用等问题进行了讨论。     

Venezuelan Rainfall Data Analysed by Using a Bayesian Space–time Model

We consider a set of data from 80 stations in the Venezuelan state of Guárico consisting of accumulated monthly rainfall in a time span of 16 years. The problem of modelling rainfall accumulated over fixed periods of time and recorded at meteorological stations at different sites is studied by using a model based on the assumption that the data follow a truncated and transformed multivariate normal distribution. The spatial correlation is modelled by using an exponentially decreasing correlation function and an interpolating surface for the means. Missing data and dry periods are handled within a Markov chain Monte Carlo framework using latent variables. We estimate the amount of rainfall as well as the probability of a dry period by using the predictive density of the data. We considered a model based on a full second-degree polynomial over the spatial co-ordinates as well as the first two Fourier harmonics to describe the variability during the year. Predictive inferences on the data show very realistic results, capturing the typical rainfall variability in time and space for that region. Important extensions of the model are also discussed.