Rényi statistics for testing equality of autocorrelation coefficients

The problem of testing for equality of autocorrelation coefficients of two populations in multivariate data when errors are autocorrelated is considered. We derive Rényi statistics defined as divergences between unrestricted and restricted estimated joint probability density functions and we show that they are asymptotically chi-square distributed under the null hypothesis of interest. Monte Carlo simulation experiments are carried out to investigate the behavior of Rényi statistics and to make comparisons with test statistics based on the approach of Bhandary [M. Bhandary, Test for equality of autocorrelation coefficients for two populations in multivariate data when the errors are autocorrelated, Statistics & Probability Letters 73 (2005) 333–342] for the problem under consideration. Rényi statistics showed to have significantly better behavior.     

论消费增长与经济增长之间的偏离

虽然经济下降的速度在放慢,但世界经济仍没有停止衰退.中国经济增长面临出口大幅下降、外需持续萎缩的严峻形势,消费需求若难以继续扩大,则政府主导投资拉动的经济增长便缺乏后劲,经济仍有下滑的可能.当前扩大内需特别是居民消费便成为保增长的当务之急.从我国建国60年消费与经济增长的偏离轨迹中可以得到印证,保持消费与经济的同步增长是保持经济短期快速增长与长期稳定发展的关键.     

基于中心-边缘理论对长三角及其周边区域经济集聚和扩散的实证研究

研究经济区域内的集聚和扩散动态趋向,对加强经济要素流动的引导,优化区域发展空间结构,提高区域竞争力,具有重要的理论和实践意义。以中心-边缘理论为基础,采用系统的分析方法,对长三角及其周边区域经济集聚和扩散运动的阶段性特征进行实证研究,发现当前集聚还处在向工业化成熟期过渡的阶段,应加强整体性的规划布局,促进区域经济协调发展。     

基于意见分歧视角的盈余公告股价效应经验研究

以A股市场上市公司定期披露的年报为样本,从投资者意见分歧的角度解释年报披露期间的股价反应,发现与意见分歧程度较低的股票相比,意见分歧程度较高的股票在年报披露期间获得显著的低收益,投资者意见分歧与年报披露期间的股票收益负相关,说明年报披露会降低投资者之间的意见分歧,导致公告前因卖空限制和意见分歧而被高估的股价在年报披露期间向其基础价值回归,这一结果符合意见分歧资产定价理论。     

An Objective Bayesian Criterion to Determine Model Prior Probabilities

We discuss the problem of selecting among alternative parametric models within the Bayesian framework. For model selection problems, which involve non‐nested models, the common objective choice of a prior on the model space is the uniform distribution. The same applies to situations where the models are nested. It is our contention that assigning equal prior probability to each model is over simplistic. Consequently, we introduce a novel approach to objectively determine model prior probabilities, conditionally, on the choice of priors for the parameters of the models. The idea is based on the notion of the worth of having each model within the selection process. At the heart of the procedure is the measure of this worth using the Kullback–Leibler divergence between densities from different models.     

On Bayesian learning via loss functions

We provide a decision theoretic approach to the construction of a learning process in the presence of independent and identically distributed observations. Starting with a probability measure representing beliefs about a key parameter, the approach allows the measure to be updated via the solution to a well defined decision problem. While the learning process encompasses the Bayesian approach, a necessary asymptotic consideration then actually implies the Bayesian learning process is best. This conclusion is due to the requirement of posterior consistency for all models and of having standardized losses between probability distributions. This is shown considering a specific continuous model and a very general class of discrete models.     

A New Family of BAN Estimators for Polytomous Logistic Regression Models based on ϕ- Divergence Measures

In this paper we study polytomous logistic regression model and the asymptotic properties of the minimum ϕ-divergence estimators for this model. A simulation study is conducted to analyze the behavior of these estimators as function of the power-divergence measure ϕ_(λ) Research partially done when was visiting the Bowling Green State University as the Distinguished Lukacs Professor     

社会必要劳动时间：马恩论述的新解读——认识分歧的客观原因探讨之二

关于社会必要劳动时间,不同意一种含义论的学者们所发现的、引以为论据的马克思恩格斯的论述,在不同地方的确存在差异.这是社会必要劳动时间含义问题上的长期存在分歧和争论的一个重要客观原因.     

Tests for Bivariate Symmetry Against Ordered Alternatives in Square Contingency Tables

Let X and Y denote two ordinal response variables, each having I levels. When subjects are classified on both variables, there are I 2 possible combinations of classifications. Let pij = Pr (X = i, Y = j) . This paper introduces a family of tests based on φ –divergence measures for testing H₀: pij = pji against H1: pij ≥ pji (I≥ j) ; and for testing H1 against H2: pij unrestricted. A simulation study assesses some of the family of tests introduced in this paper in comparison to the likelihood ratio test.     

Re-examining informative prior elicitation through the lens of Markov chain Monte Carlo methods

Summary.  In recent years, advances in Markov chain Monte Carlo techniques have had a major influence on the practice of Bayesian statistics. An interesting but hitherto largely underexplored corollary of this fact is that Markov chain Monte Carlo techniques make it practical to consider broader classes of informative priors than have been used previously. Conjugate priors, long the workhorse of classic methods for eliciting informative priors, have their roots in a time when modern computational methods were unavailable. In the current environment more attractive alternatives are practicable. A reappraisal of these classic approaches is undertaken, and principles for generating modern elicitation methods are described. A new prior elicitation methodology in accord with these principles is then presented.