On cumulative entropies

In analogy with the cumulative residual entropy recently proposed by Wang et al. [2003a. A new and robust information theoretic measure and its application to image alignment. In: Information Processing in Medical Imaging. Lecture Notes in Computer Science, vol. 2732, Springer, Heidelberg, pp. 388–400; 2003b. Cumulative residual entropy, a new measure of information and its application to image alignment. In: Proceedings on the Ninth IEEE International Conference on Computer Vision (ICCV’03), vol. 1, IEEE Computer Society Press, Silver Spring, MD, pp. 548–553], we introduce and study the cumulative entropy, which is a new measure of information alternative to the classical differential entropy. We show that the cumulative entropy of a random lifetime X can be expressed as the expectation of its mean inactivity time evaluated at X. Hence, our measure is particularly suitable to describe the information in problems related to ageing properties of reliability theory based on the past and on the inactivity times. Our results include various bounds to the cumulative entropy, its connection to the proportional reversed hazards model, and the study of its dynamic version that is shown to be increasing if the mean inactivity time is increasing. The empirical cumulative entropy is finally proposed to estimate the new information measure.     

A case–control study relating railroad worker mortality to diesel exhaust exposure using a threshold regression model

A case–control study of lung cancer mortality in U.S. railroad workers in jobs with and without diesel exhaust exposure is reanalyzed using a new threshold regression methodology. The study included 1256 workers who died of lung cancer and 2385 controls who died primarily of circulatory system diseases. Diesel exhaust exposure was assessed using railroad job history from the US Railroad Retirement Board and an industrial hygiene survey. Smoking habits were available from next-of-kin and potential asbestos exposure was assessed by job history review. The new analysis reassesses lung cancer mortality and examines circulatory system disease mortality. Jobs with regular exposure to diesel exhaust had a survival pattern characterized by an initial delay in mortality, followed by a rapid deterioration of health prior to death. The pattern is seen in subjects dying of lung cancer, circulatory system diseases, and other causes. The unique pattern is illustrated using a new type of Kaplan–Meier survival plot in which the time scale represents a measure of disease progression rather than calendar time. The disease progression scale accounts for a healthy-worker effect when describing the effects of cumulative exposures on mortality.     

A copula-based Markov chain model for the analysis of binary longitudinal data

A fully parametric first-order autoregressive (AR(1)) model is proposed to analyse binary longitudinal data. By using a discretized version of a copula, the modelling approach allows one to construct separate models for the marginal response and for the dependence between adjacent responses. In particular, the transition model that is focused on discretizes the Gaussian copula in such a way that the marginal is a Bernoulli distribution. A probit link is used to take into account concomitant information in the behaviour of the underlying marginal distribution. Fixed and time-varying covariates can be included in the model. The method is simple and is a natural extension of the AR(1) model for Gaussian series. Since the approach put forward is likelihood-based, it allows interpretations and inferences to be made that are not possible with semi-parametric approaches such as those based on generalized estimating equations. Data from a study designed to reduce the exposure of children to the sun are used to illustrate the methods.     

Multivariate regression analysis of panel data with binary outcomes applied to unemployment data

Summary  In panel studies binary outcome measures together with time stationary and time varying explanatory variables are collected over time on the same individual. Therefore, a regression analysis for this type of data must allow for the correlation among the outcomes of an individual. The multivariate probit model of Ashford and Sowden (1970) was the first regression model for multivariate binary responses. However, a likelihood analysis of the multivariate probit model with general correlation structure for higher dimensions is intractable due to the maximization over high dimensional integrals thus severely restricting ist applicability so far. Czado (1996) developed a Markov Chain Monte Carlo (MCMC) algorithm to overcome this difficulty. In this paper we present an application of this algorithm to unemployment data from the Panel Study of Income Dynamics involving 11 waves of the panel study. In addition we adapt Bayesian model checking techniques based on the posterior predictive distribution (see for example Gelman et al. (1996)) for the multivariate probit model. These help to identify mean and correlation specification which fit the data well. C. Czado was supported by research grant OGP0089858 of the Natural Sciences and Engineering Research Council of Canada.     

A class of weighted dependence measures for bivariate failure time data

This paper considers a class of summary measures of the dependence between a pair of failure time variables over a finite follow-up region. The class consists of measures that are weighted averages of local dependence measures, and includes the cross-ratio-measure and finite region version of Kendall's τ; recently proposed by the authors. Two new special cases are identified that can avoid the need to estimate the bivariate survivor function and that admit explicit variance estimators. Nonparametric estimators of such dependence measures are proposed and are shown to be consistent and asymptotically normal with variances that can be consistently estimated. Properties of selected estimators are evaluated in a simulation study, and the method is illustrated through an analysis of Australian Twin Study data.     

唐代矿业政策初论

唐王朝建立后,对矿业实行了以官营优先为前提的公私兼营政策,一方面由政府经营管理着部分矿业,另一方面也允许私人经营。对私营矿业的管理前后有所变化：唐前期,政府设冶监官营处,私人可以采矿,但要向冶监交纳矿税,而不设冶监官营处,私人亦可采矿,也可以矿产向当地政府折充课役。大约到开元九、十年稍前时,政府制定了专门的令、式条文向私营矿业征税,此后在管理上逐步予以加强。     

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.     

中华民族的理想社会及其当代启示

中华民族的理想社会是“大同社会”、“天下为公”,追求理想社会的实践活动促进了历史的发展。中华民族传统的理想社会观念对今人有如下启示：历史告诉我们,理想是我们改造社会的巨大的精神力量,纯粹的理想主义和现实主义都是不可取的;理想社会应该物质文明与精神文明并重,应该既重视制度的作用,又重视人的作用;不应以个人主义为出发点,而应将为己与为人统一起来。     

试论周作人的文学批评

周作人的文学地位不仅在于散文的贡献,而且在于文学批评的开展,他的文学批评具有西方近现代人文主义思想的特征,即对“人”的关注,并由此理解与持持新潮的出路的作家作品,开拓一些文学样式的新的美学风格,同时,周作人的文学批评又具有中国古典美学的特点,即以平淡自然为文学批评标准去衡量作家作品,其文学批评的风格也与中国传统的文评风格相似。     

西安杨官寨遗址考古的文化学再解读（上）--兼论中国当代考古文化学思潮“否定之否定”式发展

只有考古出土物上明确标着黄帝时期“字样”“图样”,或可通过别的旁证确认其为黄帝时期物件,否则,把西安杨官寨遗址与黄帝时期挂钩就是一种“比附”,这种看法并不科学,至少不了解哥德尔“不完备性定理”和阿罗“独裁定理”从形式系统角度对传统考古学局限性的揭示。杨官寨遗址出土的“镂空人面覆盆形器”（“倒扣花盆”）,即史前作为“大巫”的黄帝（或其亲属近臣）之祭器,应属“国宝”级文物;“陶祖”则是史前龙山时期代替仰韶时期过程中“制度化祭祖”的证据,显示出当时男权社会取代女权的进步。作为5000年前的“黄帝故都”,杨官寨遗址发掘近10年,除《考古报告》外,少见有分量的考古研究成果。对此,中国考古学应当直面“中国文明起源多元论”的倡导者苏秉琦、张光直等前辈当时不知杨址等新发现的缺憾,否定“中国文明起源多元论”缺乏“中国文明起源‘花芯’论”补充表述的不足,走向“否定之否定”,确认中原地区在中国文明起源中具有“花芯”即文明带头作用。