Markov bridges and enlarged filtrations

The semimartingale decomposition of a Markov chain, whose value at some future time is known, is obtained by considering an enlarged filtration.     

Simulated classical tests in multinomial probit models

This paper compares the application of different versions of the simulated counterparts of the Wald test, the score test, and the likelihood ratio test in one- and multiperiod multinomial probit models. Monte Carlo experiments show that the use of the simple form of the simulated likelihood ratio test delivers relatively robust results regarding the testing of several multinomial probit model specifications. In contrast, the inclusion of the Hessian matrix of the simulated loglikelihood function into the simulated score test and (in the multiperiod multinomial probit model) particularly the inclusion of the quasi-maximum likelihood theory into the simulated likelihood ratio test leads to substantial computational problems. The combined application of the quasi-maximum likelihood theory with the simulated Wald test or the simulated score test is not systematically superior to the application of the other versions of these two simulated classical tests either. Neither an increase in the number of observations nor in the number of random draws in the incorporated Geweke-Hajivassiliou-Keane simulator systematically lead to more precise conformities between the frequencies of type I errors and the basic significance levels. An increase in the number of observations only decreases the frequencies of type II errors, particularly regarding the simulated classical testing of multiperiod multinomial probit model specifications.     

供应链风险识别与防范策略研究

随着供应链中断事件的频繁发生及其严重后果,供应链风险管理目前成为供应链研究的一个重要方向。本文总结分析了供应链风险来源及类别,提出了供应链风险防范及其对策。     

Bayesian semiparametric inference for the accelerated failure-time model

Bayesian semiparametric inference is considered for a loglinear model. This model consists of a parametric component for the regression coefficients and a nonparametric component for the unknown error distribution. Bayesian analysis is studied for the case of a parametric prior on the regression coefficients and a mixture-of-Dirichlet-processes prior on the unknown error distribution. A Markov-chain Monte Carlo (MCMC) method is developed to compute the features of the posterior distribution. A model selection method for obtaining a more parsimonious set of predictors is studied. The method adds indicator variables to the regression equation. The set of indicator variables represents all the possible subsets to be considered. A MCMC method is developed to search stochastically for the best subset. These procedures are applied to two examples, one with censored data.     

Estimating the Ozone Depletion Potential of Methyl Bromide

Ozone depletion potential (ODP) represents the cumulative ozone depletion induced by a particular halocarbon relative to a reference gas (usually trichlorofluoromethane, CFC-11). We focus on ODP estimation for methyl bromide. Previous attempts at its estimation have assumed that components of the ODP equation are lognormally distributed. By considering a wide range of modeling scenarios, we show that this restriction (which is based on computational convenience rather than experimental evidence) has obscured the true uncertainty in the ODP value. Moreover, when publishing point estimates for the ODP value, previous authors have given either mean or median values. We submit that a more appropriate choice for a point estimate is the mode since the distribution of ODP is skewed and since the mode is by definition, the most likely value. For each modeling scenario considered, modal values are given. In general, we find these ODP point estimates are considerably lower than those published elsewhere.     

Problem Solving is Often a Matter of Cooking Up an Appropriate Markov Chain*

Abstract. By means of a series of examples, from classic contributions to probability theory as well as the author's own, an attempt is made to convince the reader that problem solving is often a matter of cooking up an appropriate Markov chain. Topics touched upon along the way include coupling, correlation inequalities, and percolation.     

中美贸易摩擦、全球价值链分工与福利效应

基于全球价值链分工,本文利用2018年中美贸易摩擦加征关税清单、世界投入产出数据库(WIOD)和中美家庭消费支出调查等数据,使用Taylor一阶展开法和考虑消费异质性的Creedy方法测算了中美贸易摩擦给全球价值链参与者带来的福利效应。研究发现:第一,无论基于Taylor一阶展开法还是Creedy方法,本轮贸易摩擦给中美两国自身带来的福利损失最大,且美国受损程度明显高于中国;第二,全球价值链上加拿大、墨西哥等北美自由贸易区(NAFTA)成员以及日本、德国、英国等经济体受中美贸易摩擦影响的福利损失较大;第三,国别责任分解表明中美福利损失主要来源于自身加税行为,而其他绝大多数经济体的损失主要由美国加税引致;第四,收入分配方面,中美贸易摩擦引致的中国福利损失是累退性的,而美国福利受损程度与收入呈现非线性的“倒U型”关系,表明中美贸易摩擦不利于收入分配格局的改善。全球价值链各参与者应秉持人类命运共同体理念,积极主动维护多边贸易规则和国际治理秩序。     

An evaluation of product commonality and group technology production methods in a pre-defined multiple-machine scenario

Group technology has been used successfully for a number of years as a setup reduction tool. It is especially valuable in low-volume high-mix manufacturing environments where products and machines may be partitioned into product families and machine cells. The partitioning of machines or processes into cells may be limited by practical constraints, and the partitioning of products is complicated as the number of products and processes increases. In this paper, the authors examine the behaviour of various grouping strategies lauded as being appropriate in situations where machine partitioning and product routeing are determined by technological constraints. Of specific experimental concern in this paper is the effect of the mean and variance of component part commonality between products on system performance under various grouping strategies in a multiple-machine environment.     

Further results in the optimal policy for imperfect inspection in discrete time

The paper reports investigations into how the optimal inspection interval for a fallible system with imperfect inspection is best determined. This work can be viewed as an extension of Raz and Ladany's 1992 work on imperfect inspection in discrete time. Here the cost functions are rewritten to include a probability conditioning that was omitted. In addition, an upper bound on the search space for the optimal inspection interval is presented. This bound allows one to find the global optimum with very modest computational effort. Insights gained from this bound and from the computational tests are used to present some general properties of the system.     

Using a Markov Chain to Construct a Tractable Approximation of an Intractable Probability Distribution

Abstract.  Let π denote an intractable probability distribution that we would like to explore. Suppose that we have a positive recurrent, irreducible Markov chain that satisfies a minorization condition and has π as its invariant measure. We provide a method of using simulations from the Markov chain to construct a statistical estimate of π from which it is straightforward to sample. We show that this estimate is 'strongly consistent' in the sense that the total variation distance between the estimate and π converges to 0 almost surely as the number of simulations grows. Moreover, we use some recently developed asymptotic results to provide guidance as to how much simulation is necessary. Draws from the estimate can be used to approximate features of π or as intelligent starting values for the original Markov chain. We illustrate our methods with two examples.