A comparison of nonparametric priors in hierarchical mixture modelling for AFT regression

We will pursue a Bayesian nonparametric approach in the hierarchical mixture modelling of lifetime data in two situations: density estimation, when the distribution is a mixture of parametric densities with a nonparametric mixing measure, and accelerated failure time (AFT) regression modelling, when the same type of mixture is used for the distribution of the error term. The Dirichlet process is a popular choice for the mixing measure, yielding a Dirichlet process mixture model for the error; as an alternative, we also allow the mixing measure to be equal to a normalized inverse-Gaussian prior, built from normalized inverse-Gaussian finite dimensional distributions, as recently proposed in the literature. Markov chain Monte Carlo techniques will be used to estimate the predictive distribution of the survival time, along with the posterior distribution of the regression parameters. A comparison between the two models will be carried out on the grounds of their predictive power and their ability to identify the number of components in a given mixture density.     

Nonparametric estimation of waiting time distributions in a Markov model based on current status data

We construct nonparametric estimators of state waiting time distribution functions in a Markov multistate model using current status data. This is a particularly difficult problem since neither the entry nor the exit times of a given state are directly observed. These estimators are obtained, using the Markov property, from estimators of counting processes of state entry and exit times, as well as, the size of “at risk” sets of state entry and transitions out of that state. Consistency of our estimators is established. Finite-sample behavior of our estimators is studied by simulation, in which we show that our estimators based on current status data compare well with those based on complete data. We also illustrate our method using a pubertal development data set obtained from the NHANES III [1997. NHANES III Reference Manuals and Reports (CD-ROM). Analytic and Reporting Guidelines: The Third National Health and Nutrition Examination Survey (1988–94). National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD] study.     

Two-dimensional generalisations of dynamic programming for image analysis

Dynamic programming (DP) is a fast, elegant method for solving many one-dimensional optimisation problems but, unfortunately, most problems in image analysis, such as restoration and warping, are two-dimensional. We consider three generalisations of DP. The first is iterated dynamic programming (IDP), where DP is used to recursively solve each of a sequence of one-dimensional problems in turn, to find a local optimum. A second algorithm is an empirical, stochastic optimiser, which is implemented by adding progressively less noise to IDP. The final approach replaces DP by a more computationally intensive Forward-Backward Gibbs Sampler, and uses a simulated annealing cooling schedule. Results are compared with existing pixel-by-pixel methods of iterated conditional modes (ICM) and simulated annealing in two applications: to restore a synthetic aperture radar (SAR) image, and to warp a pulsed-field electrophoresis gel into alignment with a reference image. We find that IDP and its stochastic variant outperform the remaining algorithms.     

Forecasting obesity trends in England

Summary.  Forecasts of trends in obesity in England for 2010 are produced by treating the available data, which contain the proportions of the population, categorized by age and sex, falling into different body mass index ranges, as compositional data sets, so that the implicit simplex restrictions are automatically satisfied. Forecasts are calculated by using linear trend models for the log-ratio transformations and are accompanied by prediction regions. The advantages of treating data on proportions compositionally are emphasized and compared with forecasts that have been obtained by ignoring this restriction.     

休闲是人的全面发展的实践形态——试论马克思休闲思想及其当代意义

马克思的休闲思想主要体现在他的自由时间理论中,自由时间是由于社会生产力的发展,必要劳动时间缩短而游离出来的。自．由时间为人提供自由存在和自由发展的社会空间,自由时间是人的自由全面发展的必要条件。休闲是人在自由时间中,通过一系列自由选择的活动探索生命的意义,是人“成为人”的过程,是人的自由全面发展的实践形态。     

马克思"自由"意蕴中的休闲思想

马克思将自己的理论工作定位为"批判旧世界中发现新世界",他的休闲思想正是隐喻在这种"批判"与"发现"中。这种隐喻可以从马克思对于自由时间、自由活动和人的自由全面发展的论述中得到指认和证明。     

从马克思主义的国家学说看达赖集团分裂祖国的反动本质

西藏自古以来就是中国的一部分,这无论是过去还是现在都是全世界各国所公认的事实.以达赖为首的民族分裂集团,自1959年叛逃后,在国外长期从事民族分裂活动,利用国际上提供的各种场合,到处宣称"西藏自古以来是一个独立国家"的反动谬论,利用混淆古代国家与现代国家的概念,不仅搞乱了人民群众的思想,模糊了人民群众的视线,同时也造成了极大的危害,为其推行"民族独立"的分裂活动张目.我们必须以马克思主义国家学说为指导,牢固掌握西藏自古以来就是中国的一部分的科学内涵,牢固树立正确的马克思主义祖国观,以实际行动抵制达赖的民族分裂活动,积极维护祖国统一和民族团结,为建设团结、富裕、文明、和谐的社会主义新西藏而努力奋斗.     

浅析音乐节奏对体育运动的影响

随着体育运动的日益发展,音乐作为体育运动的一种表现形式和运动训练的辅助手段,已逐步被一些项目所采用.为搞清音乐与体育的结合点,通过音乐与体育的节奏性特点,阐述了音乐的节奏性规律对体育运动的影响.在某些体育运动项目中采用音乐伴奏可以提高运动成绩.同时,音乐又以它独有的魅力直接表达体育的美.分析音乐与体育的结合机制,有利于进一步促进音乐在体育领域里的应用.并且,音乐对培养运动节奏感有很大帮助.     

关于养老“时间储蓄”的问题与思考

近年来养老“时间储蓄”成为人们关注的热点问题。通过调查和访谈 ,本文分析了“时间储蓄”的内涵 ,指出了目前存在的问题 ,并从经济学和社会学两种视角审视“时间储蓄”的社会效用 ,说明“时间储蓄”面临的困难和对文化制度建设的意义 ,结论认为“时间储蓄”的可行性和应用性还有待于进一步的思考和论证 ,需要在正确的认识指导下确定详细的实施方案并在一定区域内试点 ,进一步总结经验 ,证明其自身的价值