Conservative prior distributions for variance parameters in hierarchical models

Bayesian hierarchical models typically involve specifying prior distributions for one or more variance components. This is rather removed from the observed data, so specification based on expert knowledge can be difficult. While there are suggestions for “default” priors in the literature, often a conditionally conjugate inverse‐gamma specification is used, despite documented drawbacks of this choice. The authors suggest “conservative” prior distributions for variance components, which deliberately give more weight to smaller values. These are appropriate for investigators who are skeptical about the presence of variability in the second‐stage parameters (random effects) and want to particularly guard against inferring more structure than is really present. The suggested priors readily adapt to various hierarchical modelling settings, such as fitting smooth curves, modelling spatial variation and combining data from multiple sites.     

Objective Testing Procedures in Linear Models: Calibration of the p-values

Abstract.  An optimal Bayesian decision procedure for testing hypothesis in normal linear models based on intrinsic model posterior probabilities is considered. It is proven that these posterior probabilities are simple functions of the classical F -statistic, thus the evaluation of the procedure can be carried out analytically through the frequentist analysis of the posterior probability of the null. An asymptotic analysis proves that, under mild conditions on the design matrix, the procedure is consistent. For any testing hypothesis it is also seen that there is a one-to-one mapping – which we call calibration curve – between the posterior probability of the null hypothesis and the classical bi p -value. This curve adds substantial knowledge about the possible discrepancies between the Bayesian and the p -value measures of evidence for testing hypothesis. It permits a better understanding of the serious difficulties that are encountered in linear models for interpreting the p -values. A specific illustration of the variable selection problem is given.     

论科技期刊编辑出版工作中的著作权保护

以科技期刊编辑出版工作中的环节为主线,分别阐述了组稿、审稿、编辑加工、出版管理等4个环节中所涉及的有关著作权保护问题。     

Bayesian Survival Analysis Using Bernstein Polynomials

Abstract.  Bayesian survival analysis of right-censored survival data is studied using priors on Bernstein polynomials and Markov chain Monte Carlo methods. These priors easily take into consideration geometric information like convexity or initial guess on the cumulative hazard functions, select only smooth functions, can have large enough support, and can be easily specified and generated. Certain frequentist asymptotic properties of the posterior distribution are established. Simulation studies indicate that these Bayes methods are quite satisfactory.     

析《"新莱茵报"审判案》之法律思想

马克思在《"新莱茵报"审判案》中关于"立法权"应与"执行权"分离的思想,他适用法律时的方法论,他关于官员"执行职务"与"不执行职务"的区分,对出版自由与政治关系的表述",对我国的立法实践都有其重要的指导意义。恩格斯于审判案中体现出的对"法律时代性"、"陪审员职责"等思想同样具有指导性。     

老舍编辑出版活动及其理论述论

老舍是编辑家,是20世纪中国最具编辑风格和贡献的编辑家之一,及藉编辑出版推进通俗文艺普及与提高的卓越旗手。致力文艺出版物通俗化、民族化、大众化及中国化,是老舍编辑出版活动的主要贡献。老舍编辑出版理论与其文艺创作理论水乳交融,形成了同一的创作—编辑美学理论。他把创作理想延续至编辑出版理想,赋予编辑出版活动人性化人格化“理想”,将理念和理想贯通于创作、编辑、出版、发行整个精神产品生产的过程。老舍编辑出版活动及其理论是中国编辑史出版史上不容忽视的文化遗产。     

阳光司法:从刘涌案谈判决书的论证与公开

刘涌案引起公众大量非议的重要原因是二审判决书论证不足和公开性不够.加强判决书的说理和论证并向公众公开是树立司法权威、建立为人民服务司法和实现理论界与实务界良性互动所必要的.判决书的论证应从证据能力、证明力、证据的综合认定及法律适用几方面进行.公开判决书可通过网上公布及公共场合展示来实现.     

论信息不对称对政府信用的影响

分析了我国政府与公众信息不对称的现状及对政府信用的影响,信息不对称违背公民的知情权及以民众为本位的宗旨,导致行政神秘化,易诱发腐败且影响公众对政府的监督和约束,影响公众参政议政.指出解决信息不对称问题是建设诚信、开放、透明的政府的重要保证.     

保卫苗侗民族的宝贵历史记忆库——黔东南“锦屏文书”抢救保护的回顾与展望

自2007年以来,黔东南苗族侗族自治州档案部门在各级党委和政府的支持下,投入了大量人力、物力和财力到"锦屏文书"("清水江文书")抢救保护工作中,取得了可观的成效。本文总结了其中的经验教训,认为以往工作中存在一定程度上的"重抢救保护、轻编研出版"的倾向,提出今后不仅要继续重视抢救保护,同时也要在编研出版和开发利用方面齐头并进,力争使"锦屏文书"早日成功申报联合国"世界记忆名录",成为苗侗人民的世界文化品牌。     

Objective Bayesian Analysis of an Exponential Regression Model with Constrained Parameters Applied to Animal Digestibility

In animal digestibility the proportion of degraded food along the time has usually been modeled as a normal random variable with mean a function of the time and the following three parameters: the proportion of degraded food almost instantaneously, remaining proportion of food to be degraded, and velocity of degradation. The estimation of these parameters has been carried out mainly from a frequentist viewpoint by using the asymptotic distribution of the maximum likelihood estimator. This may give inadmissible estimates, such as values outside of the range of the parameters. This drawback could not appear if a Bayesian approach were adopted. In this article an objective Bayesian analysis is developed and illustrated on real and simulated data.