On an adaptive version of the Metropolis–Hastings algorithm with independent proposal distribution

In this paper, we present a general formulation of an algorithm, the adaptive independent chain (AIC), that was introduced in a special context in Gåsemyr et al . [ Methodol. Comput. Appl. Probab. 3 (2001)]. The algorithm aims at producing samples from a specific target distribution Π, and is an adaptive, non-Markovian version of the Metropolis–Hastings independent chain. A certain parametric class of possible proposal distributions is fixed, and the parameters of the proposal distribution are updated periodically on the basis of the recent history of the chain, thereby obtaining proposals that get ever closer to Π. We show that under certain conditions, the algorithm produces an exact sample from Π in a finite number of iterations, and hence that it converges to Π. We also present another adaptive algorithm, the componentwise adaptive independent chain (CAIC), which may be an alternative in particular in high dimensions. The CAIC may be regarded as an adaptive approximation to the Gibbs sampler updating parametric approximations to the conditionals of Π.     

Random variate transformations in the Gibbs sampler: issues of efficiency and convergence

In the non-conjugate Gibbs sampler, the required sampling from the full conditional densities needs the adoption of black-box sampling methods. Recent suggestions include rejection sampling, adaptive rejection sampling, generalized ratio of uniforms, and the Griddy-Gibbs sampler. This paper describes a general idea based on variate transformations which can be tailored in all the above methods and increase the Gibbs sampler efficiency. Moreover, a simple technique to assess convergence is suggested and illustrative examples are presented.     

A Five-Decision Testing Procedure to Infer the Value of a Unidimensional Parameter

ABSTRACT

A statistical test can be seen as a procedure to produce a decision based on observed data, where some decisions consist of rejecting a hypothesis (yielding a significant result) and some do not, and where one controls the probability to make a wrong rejection at some prespecified significance level. Whereas traditional hypothesis testing involves only two possible decisions (to reject or not a null hypothesis), Kaiser’s directional two-sided test as well as the more recently introduced testing procedure of Jones and Tukey, each equivalent to running two one-sided tests, involve three possible decisions to infer the value of a unidimensional parameter. The latter procedure assumes that a point null hypothesis is impossible (e.g., that two treatments cannot have exactly the same effect), allowing a gain of statistical power. There are, however, situations where a point hypothesis is indeed plausible, for example, when considering hypotheses derived from Einstein’s theories. In this article, we introduce a five-decision rule testing procedure, equivalent to running a traditional two-sided test in addition to two one-sided tests, which combines the advantages of the testing procedures of Kaiser (no assumption on a point hypothesis being impossible) and Jones and Tukey (higher power), allowing for a nonnegligible (typically 20%) reduction of the sample size needed to reach a given statistical power to get a significant result, compared to the traditional approach.     

文献资源建设中的剔除环节──文献资源建设理论与实践研讨之二

剔除不仅具有清除陈旧失效文献、减轻工作人员负担、减少书库压力、缓解空间紧张的作用，在文献资源建设中还能发挥净化、优化功能；活化功能；控制功能；反馈功能；检验功能；复苏功能。这就使得剔除成为文献资源建设中不可缺少的重要环节。剔除没有绝对的统一的标准，一般只能以文献的内容、外形、文献的年龄以及使用情况为依据，综合归纳拟定出适用于自己馆的文献剔除标准和范围。剔除的方法多种多样，最好将几种方法结合使用，取长补短，效果会更好些。     

Kullback-leibler information approach to the optimum measurement point for bayesian estimation

When an appropriate parametric model and a prior distribution of its parameters are given to describe clinical time courses of a dynamic biological process, Bayesian approaches allow us to estimate the entire profiles from a few or even a single observation per subject. The goodness of the estimation depends on the measurement points at which the observations were made. The number of measurement points per subject is generally limited to one or two. The limited measurement points have to be selected carefully. This paper proposes an approach to the selection of the optimum measurement point for Bayesian estimations of clinical time courses. The selection is made among given candidates, based on the goodness of estimation evaluated by the Kullback-Leibler information. This information measures the discrepancy of an estimated time course from the true one specified by a given appropriate model. The proposed approach is applied to a pharmacokinetic analysis, which is a typical clinical example where the selection is required. The results of the present study strongly suggest that the proposed approach is applicable to pharmacokinetic data and has a wide range of clinical applications.     

参数假设检验教学中的几个问题

在概率统计教学的基础上,对参数假设检验教学中出现的学生难于理解的几个问题做了系统的总结,主要包括决策的依据,拒绝域的选择以及决策可靠性的判断,其中对做出"接受零假设"决策的可靠性给出了解释。     

利益威胁、政府工作满意度与市民对进城农民的接纳意愿——基于CSS2011数据的实证分析

本文讨论了当代中国城市本地人排斥进城农民这一问题的发生机制。通过剖析经典利益威胁假说，依托冲突理论以及中国的现实情境，本研究考察了市民与政府关系的演化及其对排斥进城农民的影响。通过对“2011年中国社会状况综合调查”数据的分析，本研究获得以下发现：（1）本地市民对进城农民的排斥部分源于其当前生活境遇的恶化，但更主要的原因是过去的利益受损经历以及对未来社会地位变化的悲观预期；（2）本地市民对政府供给公共服务的不满，会降低其对进城农民的接纳意愿；（3）在本地市民中，遭受利益损失的“失意者”对进城农民的排斥态度显著受到其对政府行政能力不满以及政府解决社会不平等问题低效的影响。本研究证实了市民对进城农民的排斥意愿中存在“替罪羊”机制。     

人类中心主义:拒斥抑或遗继?

生态伦理的合理性论证是基于对人类中心主义的批判和超越旨趣的 ,然而 ,在一些非人类中心主义的生态伦理拓展、合理性论证以及建构过程中 ,却不同程度地存在着一个无意识的“中心情结”。这种“中心情结” ,正是人类中心主义在被批判表象下的深层表达和遗继。这种无意识的“中心情结”的存在 ,不仅会导致对人类中心主义的批判力的下降 ,而且也将使自身的合理性受到怀疑。因此 ,对于一个要彻底走出人类中心的真正意义上的生态伦理 ,就必须摆脱生态伦理论证过程中存在的非自觉无意识的“中心情结”桎梏 ,并找回生态伦理中被遗忘的人的本真意义     

浅析科技期刊编辑素养与退稿的关系

科技期刊编辑素养充分体现在初审退稿工作以及退稿信的写作中。针对退稿的主客观原因和“退稿不退人”的编辑原则,只有加强编辑工作者的政治、思想道德、科学文化和业务能力等各方面的素养,才能做好科技期刊退稿工作,保证期刊的质量。     

An iterative Monte Carlo method for nonconjugate Bayesian analysis

The Gibbs sampler has been proposed as a general method for Bayesian calculation in Gelfand and Smith (1990). However, the predominance of experience to date resides in applications assuming conjugacy where implementation is reasonably straightforward. This paper describes a tailored approximate rejection method approach for implementation of the Gibbs sampler when nonconjugate structure is present. Several challenging applications are presented for illustration.