Bayesian analysis of elapsed times in continuous‐time Markov chains

The authors consider Bayesian analysis for continuous‐time Markov chain models based on a conditional reference prior. For such models, inference of the elapsed time between chain observations depends heavily on the rate of decay of the prior as the elapsed time increases. Moreover, improper priors on the elapsed time may lead to improper posterior distributions. In addition, an infinitesimal rate matrix also characterizes this class of models. Experts often have good prior knowledge about the parameters of this matrix. The authors show that the use of a proper prior for the rate matrix parameters together with the conditional reference prior for the elapsed time yields a proper posterior distribution. The authors also demonstrate that, when compared to analyses based on priors previously proposed in the literature, a Bayesian analysis on the elapsed time based on the conditional reference prior possesses better frequentist properties. The type of prior thus represents a better default prior choice for estimation software.     

审判委员会制度的理论架构

对审委会制度的改革,有人主张废除审委会制度为其核心内容;也有人主张目前应在保留的基础上予以改革和完善。废改之争,焦点是研究方法之争。主废和主改各有其弊,应通过立法程序按照“不审不判”的思路重塑审委会制度,明确审委会审判组织的地位,赋予其合议、陪审、咨询、调研四大职能及案件实体决定权和指导权,并建立和完善相应的程序。     

《 转运汉巧遇洞庭红》的商业解读

凌濛初<转运汉巧遇洞庭红>通过对金老汉藏财失财和文若虚随人出海,两次巧遇发财机会陡然暴富故事的描写,反映了明代中叶以后商业发展的具体场景,反映了关于相互需求的贸易原理和资本增殖的思想.通过文若虚形象的塑造,反映了作者经商和为人相统一的思想.从故事可知,明中叶后商业发展已经有了一定的规模和规范,但也还有一些不利于商业贸易更大发展的因素,如储运和金融手段的滞后等.     

价值链理论在跨国经营中的应用

本文剖析了价值链的特点及其变化趋势,阐明了在跨国经营中如何应用价值链理论。旨在为提高我国企业的国际经济竞争力提供参考。     

高等教育产业化过程中的价值取向问题

目前,高等教育产业化在发展过程中对高等教育产生了消极影响。对高等教育价值的不正确的认识,是产生这些消极影响的重要原因。在高等教育产业化继续发展的进程中,我们应该坚持正确的高等教育价值取向。     

实物期权与DCF法在评估创业企业价值中的比较分析

创业企业价值评估对于投资决策具有十分重要的意义。但是,目前常用的折现现金流量法无法完整地评估创业企业价值。因此,本文引入实物期权概念对创业企业价值进行分析,并运用Black-Scholes公式对其价值进行评估。     

中国共产党形势政策观的基本价值取向初探

党的形势政策观的价值取向,就是我党在分析形势进行政策活动时所指向的一定价值目标的活动过程。中国共产党在长期革命和建设实践中,总是以解放和发展生产力、代表人民群众的根本利益以及维护工人阶级的统治地位和阶级利益作为党的基本价值取向,它们同时也是党在分析形势进行政策活动过程中的基本价值取向。     

Causal Reasoning from Longitudinal Data*

Abstract. This paper reviews some of the key statistical ideas that are encountered when trying to find empirical support to causal interpretations and conclusions, by applying statistical methods on experimental or observational longitudinal data. In such data, typically a collection of individuals are followed over time, then each one has registered a sequence of covariate measurements along with values of control variables that in the analysis are to be interpreted as causes, and finally the individual outcomes or responses are reported. Particular attention is given to the potentially important problem of confounding. We provide conditions under which, at least in principle, unconfounded estimation of the causal effects can be accomplished. Our approach for dealing with causal problems is entirely probabilistic, and we apply Bayesian ideas and techniques to deal with the corresponding statistical inference. In particular, we use the general framework of marked point processes for setting up the probability models, and consider posterior predictive distributions as providing the natural summary measures for assessing the causal effects. We also draw connections to relevant recent work in this area, notably to Judea Pearl's formulations based on graphical models and his calculus of so‐called do‐probabilities. Two examples illustrating different aspects of causal reasoning are discussed in detail.     

经典危言与文学价值的迷失

文学经典是对宗教一政治语境中的"经典"的一种移用,但文学经典因移用而来的神圣化之原初语义遭遇了当代祛魅,由此嬗生的"经典消亡"论,其实质在于对文学价值的怀疑或搁置.祛魅的文学经典不再神圣,但却将作为价值基准而持续在场,因此经典并不会消亡.     

Conditional Likelihood Estimators for Hidden Markov Models and Stochastic Volatility Models

ABSTRACT.  This paper develops a new contrast process for parametric inference of general hidden Markov models, when the hidden chain has a non-compact state space. This contrast is based on the conditional likelihood approach, often used for ARCH-type models. We prove the strong consistency of the conditional likelihood estimators under appropriate conditions. The method is applied to the Kalman filter (for which this contrast and the exact likelihood lead to asymptotically equivalent estimators) and to the discretely observed stochastic volatility models.