供应链系统视角下消费者环境意识的社会分配效应研究

消费者环境意识既会促进环境保护,又会改变消费行为、市场需求、消费者福利和企业利润等的分配。然而,消费者环境意识的分配效应是环境保护、消费行为和社会分配三者的结合部,在现有研究中鲜有涉及。对此,从供应链系统视角建立博弈模型,求解供应链定价和生态技术投资决策,分析消费者环境意识逐级传导引起的连锁反应,研究消费者环境意识如何改变社会分配格局。研究发现：首先,消费者环境意识会提高市场价格和企业利润,但是会降低消费者福利;其次,消费者环境意识会提高企业的分配地位,其中对零售企业的提高程度更大,但是会降低消费者的分配地位;最后,消费者环境意识越强,企业分配地位的提高程度和消费者福利的下降程度就越大。本研究丰富了对消费者环境意识多种作用的认识,有利于合理权衡环保、经济和分配等制定科学合理的发展政策。     

风险规避情形下第一价格拍卖的非参数估计方法

在拍卖实证研究的大量文献中,越来越多的证据表明投标人更趋向于风险规避,然而目前拍卖计量方法通常只考虑风险中性的情形。针对这一问题,推广了针对第一价格拍卖的非参数估计方法,给出了多个风险规避参数估计量来处理风险规避的情形,并总结了相应的估计过程。为验证估计效果,蒙特卡罗模拟实验被用于进行分析和评价。实验结果表明,无论是对风险规避参数,还是私有估值的估计,总体上都能取得不错的效果,验证了方法的有效性,同时为风险规避参数估计量的选择提供了一些指导性建议。     

Signed likelihood ratio tests in the Birnbaum-Saunders regression model

The Birnbaum-Saunders regression model is becoming increasingly popular in lifetime analyses and reliability studies. In this model, the signed likelihood ratio statistic provides the basis for testing inference and construction of confidence limits for a single parameter of interest. We focus on the small sample case, where the standard normal distribution gives a poor approximation to the true distribution of the statistic. We derive three adjusted signed likelihood ratio statistics that lead to very accurate inference even for very small samples. Two empirical applications are presented.     

Inference for Epidemics with Three Levels of Mixing: Methodology and Application to a Measles Outbreak

Abstract. A stochastic epidemic model is defined in which each individual belongs to a household, a secondary grouping (typically school or workplace) and also the community as a whole. Moreover, infectious contacts take place in these three settings according to potentially different rates. For this model, we consider how different kinds of data can be used to estimate the infection rate parameters with a view to understanding what can and cannot be inferred. Among other things we find that temporal data can be of considerable inferential benefit compared with final size data, that the degree of heterogeneity in the data can have a considerable effect on inference for non‐household transmission, and that inferences can be materially different from those obtained from a model with only two levels of mixing. We illustrate our findings by analysing a highly detailed dataset concerning a measles outbreak in Hagelloch, Germany.     

Detecting mean increases in Poisson INAR(1) processes with EWMA control charts

Processes of serially dependent Poisson counts are commonly observed in real-world applications and can often be modeled by the first-order integer-valued autoregressive (INAR) model. For detecting positive shifts in the mean of a Poisson INAR(1) process, we propose the one-sided s exponentially weighted moving average (EWMA) control chart, which is based on a new type of rounding operation. The s-EWMA chart allows computing average run length (ARLs) exactly and efficiently with a Markov chain approach. Using an implementation of this procedure for ARL computation, the s-EWMA chart is easily designed, which is demonstrated with a real-data example. Based on an extensive study of ARLs, the out-of-control performance of the chart is analyzed and compared with that of a c chart and a one-sided cumulative sum (CUSUM) chart. We also investigate the robustness of the chart against departures from the assumed Poisson marginal distribution.     

Estimation using plug-in of the stationary distribution and Shannon entropy of continuous time Markov processes

A natural way to deal with the uncertainty of an ergodic finite state space Markov process is to investigate the entropy of its stationary distribution. When the process is observed, it becomes necessary to estimate this entropy.We estimate both the stationary distribution and its entropy by plug-in of the estimators of the infinitesimal generator. Three situations of observation are discussed: one long trajectory is observed, several independent short trajectories are observed, or the process is observed at discrete times. The good asymptotic behavior of the plug-in estimators is established. We also illustrate the behavior of the estimators through simulation.     

投资组合动态VaR预测模型和预测精度评价

采用4种Backtesting检验方法,检验22个常态和时变投资组合动态VaR预测模型的风险预测精度,发现GJR_GPD_TV_Copula具有最高的投资组合风险预测精度,GJR_GPD_Copula的拟合、密度预测和组合风险预测精度都要高于GJR_SKST_Copula,且Copula模型的组合风险预测精度分别与拟合精度和密度预测精度存在较弱的正相关关系.     

A Bayesian Model for Markov Chains via Jeffrey's Prior

Abstract

This work deals with the problem of Bayesian estimation of the transition probabilities associated with multistate Markov chain. The model is based on the Jeffreys' noninformative prior. The Bayesian estimator is approximated by means of MCMC techniques. A numerical study by simulation is done in order to compare the Bayesian estimator with the maximum likelihood estimator.     

On algorithmic construction of maximin distance designs

Maximin distance designs are useful for conducting expensive computer experiments. In this article, we compare some global optimization algorithms for constructing such designs. We also introduce several related space-filling designs, including nested maximin distance designs, sliced maximin distance designs, and general maximin distance designs with better projection properties. These designs possess more flexible structures than their analogs in the literature. Examples of these designs constructed by the algorithms are presented.     

Inference in hybrid Bayesian networks using dynamic discretization

We consider approximate inference in hybrid Bayesian Networks (BNs) and present a new iterative algorithm that efficiently combines dynamic discretization with robust propagation algorithms on junction trees. Our approach offers a significant extension to Bayesian Network theory and practice by offering a flexible way of modeling continuous nodes in BNs conditioned on complex configurations of evidence and intermixed with discrete nodes as both parents and children of continuous nodes. Our algorithm is implemented in a commercial Bayesian Network software package, AgenaRisk, which allows model construction and testing to be carried out easily. The results from the empirical trials clearly show how our software can deal effectively with different type of hybrid models containing elements of expert judgment as well as statistical inference. In particular, we show how the rapid convergence of the algorithm towards zones of high probability density, make robust inference analysis possible even in situations where, due to the lack of information in both prior and data, robust sampling becomes unfeasible.