A new approximation of the posterior distribution of the log–odds ratio

In this paper, the posterior density of the log–odds ratio is studied. It is assumed that the observations have a multinomial distribution and that the prior on the multinomial parameters is a Dirichlet density. Several approximations currently available are reviewed. Under certain conditions on the prior parameters of the Dirichlet density, it is shown that the posterior moments can be computed exactly. A new approximation, similar to the Edgeworth expansion is also proposed. Using a numerical example, the different methods of approximation of posterior density are compared.     

Gamma-admissibility of estimators in the one-parameter exponential family

Summary Admissibility of estimators under vague prior information on the distribution of the unknown parameter is studied which leads to the notion of gamma-admissibility. A sufficient condition for an estimator of the formδ(x)=(ax+b)/(cx+d) to be gamma-admissible in the one-parameter exponential family under squared error loss is established. As an application of this result two equalizer rules are shown to be unique gamma-minimax estimators by proving their gamma-admissibility.     

大数据“杀熟”的法律规制研究

大数据"杀熟"通常表现为差别定价、价格欺诈、特定推送等形式。从公民基本权益保障的角度来看,大数据"杀熟"侵犯了消费者的个人信息权、公平交易权、知悉真情权和自主选择权。文章认为我国当前的法律体系在应对大数据"杀熟"问题上呈现出相当大的矛盾性和滞后性,难以有效解决大数据"杀熟"产生的法律问题。因此,建立数据收集处理、算法规制的事前审查评估法律体系以及分区域执法、消费者司法保护的事后严格监管法律体系,是规制大数据"杀熟"、维护公民基本权益、制约算法威胁的有效路径。     

创业者先前知识对创业绩效的影响——环境动态性的调节作用

基于西安高新技术产业开发区的165家创业企业的调查数据,运用SPSS13.0软件分析创业者先前知识的两个维度——技术相关知识和市场相关知识与创业绩效的关系,并探究环境动态性对上述关系的调节作用。进一步将创业绩效分为成长绩效和财务绩效,分析技术相关知识对它们的影响作用。研究结论如下:创业者的先前技术相关知识对创业绩效具有负向影响,先前市场相关知识对创业绩效具有正向作用;环境动态性对创业者先前技术相关知识与创业绩效的关系具有负向调节作用,对创业者先前市场相关知识与创业绩效的关系具有正向调节作用;技术相关知识对成长绩效具有正向作用,对财务绩效具有负向作用。     

Ad-induced affect: The effects of forewarning,affect intensity,and prior brand attitude

Emotion in marketing communication is important because it influences the manner consumers process information. Using emotional appeal ads, a between-subjects experiment was conducted to examine the role of forewarning of persuasive intent, affect intensity, and prior attitude. Results indicate that forewarning of persuasive intent of the advertiser had negative attitudinal effects on the dependent variables regardless of experimental conditions. Forewarning of persuasive intent had negative attitudinal effects even among participants who had positive attitudes toward the company, and those who had high affect intensity. Results also indicate that participants who already had negative attitudes toward the brand were not influenced by ad-induced affect.     

Modelling long-term investment returns via Bayesian infinite mixture time series models

This paper introduces the class of Bayesian infinite mixture time series models first proposed in Lau & So (2004) for modelling long-term investment returns. It is a flexible class of time series models and provides a flexible way to incorporate full information contained in all autoregressive components with various orders by utilizing the idea of Bayesian averaging or mixing. We adopt a Bayesian sampling scheme based on a weighted Chinese restaurant process for generating partitions of investment returns to estimate the Bayesian infinite mixture time series models. Instead of using the point estimates, as in the classical or non-Bayesian approach, the estimation in this paper is performed by the full Bayesian approach, utilizing the idea of Bayesian averaging to incorporate all information contained in the posterior distributions of the random parameters. This provides a natural way to incorporate model risk or uncertainty. The proposed models can also be used to perform clustering of investment returns and detect outliers of returns. We employ the monthly data from the Toronto Stock Exchange 300 (TSE 300) indices to illustrate the implementation of our models and compare the simulated results from the estimated models with the empirical characteristics of the TSE 300 data. We apply the Bayesian predictive distribution of the logarithmic returns obtained by the Bayesian averaging or mixing to evaluate the quantile-based and conditional tail expectation risk measures for segregated fund contracts via stochastic simulation. We compare the risk measures evaluated from our models with those from some well-known and important models in the literature, and highlight some features that can be obtained from our models.     

Empiric bayes estimation of hypergeometric probability

A Bayes-empiric Bayes estimator of a parameter of the hypergeometric distribution, based on orthogonal polynomials on non-negative integers, is introduced. It is shown that this estimator is asymptotically optimal; and the resulting estimator of the prior probability function is mean square consistent.     

基于贝叶斯决策的先验分布的选取方法

通过引入损失函数和风险函数，把选择一个合理的先验看做是一个贝叶斯决策问题。并且在“0—1”损失函数的情况下．后验似然合理先验就是所提出的最小风险合理先验。此外，在一定的条件下，ML-Ⅱ先验与最小风险合理先验是等价的。     

新中国优先发展重工业战略的形成背景、主要经验及其现实意义

中国工业化以“一五”计划的制定与实施为起点刘少奇、周恩来、陈云等党和国家领导人对中国工业化道路提出了不同的思考。从理论上看,中国工业化道路一开始选择优先发展重工业战略是与马克思的社会再生产理论以及与列宁进一步归纳和发展的“生产资料生产优先增长规律”密切相关。朝鲜战争的爆发加速了我国优先发展重工业战略的形成。保证重点、均衡发展是“一五”时期所实施的优先发展重工业战略的最显著的特点,同时也是实施优先发展重工业战略的成功经验。     

Bayesian approach to LR assessment in case of rare type match

The likelihood ratio (LR) is largely used to evaluate the relative weight of forensic data regarding two hypotheses, and for its assessment, Bayesian methods are widespread in the forensic field. However, the Bayesian ‘recipe’ for the LR presented in most of the literature consists of plugging‐in Bayesian estimates of the involved nuisance parameters into a frequentist‐defined LR: frequentist and Bayesian methods are thus mixed, giving rise to solutions obtained by hybrid reasoning. This paper provides the derivation of a proper Bayesian approach to assess LRs for the ‘rare type match problem’, the situation in which the expert wants to evaluate a match between the DNA profile of a suspect and that of a trace from the crime scene, and this profile has never been observed before in the database of reference. LR assessment using the two most popular Bayesian models (beta‐binomial and Dirichlet‐multinomial) is discussed and compared with corresponding plug‐in versions.