On the performance of the Bayesian composite likelihood estimation of max-stable processes

The max-stable process is a natural approach for modelling extrenal dependence in spatial data. However, the estimation is difficult due to the intractability of the full likelihoods. One approach that can be used to estimate the posterior distribution of the parameters of the max-stable process is to employ composite likelihoods in the Markov chain Monte Carlo (MCMC) samplers, possibly with adjustment of the credible intervals. In this paper, we investigate the performance of the composite likelihood-based MCMC samplers under various settings of the Gaussian extreme value process and the Brown–Resnick process. Based on our findings, some suggestions are made to facilitate the application of this estimator in real data.     

Strong law of large numbers of the delayed sums for Markov Chains indexed by a Cayley tree

Abstract

In this paper, we will study the strong law of large numbers of the delayed sums for Markov chains indexed by a Cayley tree with countable state spaces. Firstly, we prove a strong limit theorem for the delayed sums of the bivariate functions for Markov chains indexed by a Cayley tree. Secondly, the strong law of large numbers for the frequencies of occurrence of states of the delayed sums is obtained. As a corollary, we obtain the strong law of large numbers for the frequencies of occurrence of states for countable Markov chains indexed by a Cayley tree.     

Toward a Typology of Buyer–Supplier Relationships: A Study of the Computer Industry*

Buyer–supplier relationship typologies are useful analytical tools for purchasing managers in managing exchange relationships with suppliers and monitoring their purchasing portfolios. Existing buyer–supplier relationship typologies are mainly focused on either relational contents or power‐dependence and have limited empirical support for their performance implications. In this study, we developed an alternative buyer–supplier relationship typology that integrates both relational content and power‐dependence dimensions, resulting in four generic relationship types: market, power, autonomous‐link, and constrained‐link relationships. We then performed a longitudinal exploratory investigation of eight leading firms in the U.S. computer industry to explore the performance implications of the typology, using a combinatorial qualitative approach that leverages the strengths of case study research, content analysis, and quasi‐experimental design. The results suggest three theoretical propositions. First, the association between the type of buyer–supplier relationships and buyer firm performance varies such that constrained‐link relationships are superior in terms of operational efficiency while autonomous‐link relationships are superior in terms of product innovation. Second, the positive association between buyer–supplier relational contents (i.e., relationalism) and buyer firm operational efficiency is strengthened as the suppliers' dependence on the buyer firm increases. And finally, the positive association between buyer–supplier relationalism and buyer firm product innovation is weakened as the suppliers' dependence on the buyer firm increases.     

What Happens When Wal‐Mart Comes to Town: An Empirical Analysis of the Discount Retailing Industry

In the past few decades multistore retailers, especially those with 100 or more stores, have experienced substantial growth. At the same time, there is widely reported public outcry over the impact of these chain stores on other retailers and local communities. This paper develops an empirical model to assess the impact of chain stores on other discount retailers and to quantify the size of the scale economies within a chain. The model has two key features. First, it allows for flexible competition patterns among all players. Second, for chains, it incorporates the scale economies that arise from operating multiple stores in nearby regions. In doing so, the model relaxes the commonly used assumption that entry in different markets is independent. The lattice theory is exploited to solve this complicated entry game among chains and other discount retailers in a large number of markets. It is found that the negative impact of Kmart's presence on Wal‐Mart's profit was much stronger in 1988 than in 1997, while the opposite is true for the effect of Wal‐Mart's presence on Kmart's profit. Having a chain store in a market makes roughly 50% of the discount stores unprofitable. Wal‐Mart's expansion from the late 1980s to the late 1990s explains about 40–50% of the net change in the number of small discount stores and 30–40% for all other discount stores. Scale economies were important for Wal‐Mart, but less so for Kmart, and the magnitude did not grow proportionately with the chains' sizes.     

供应链中捆绑式数量折扣合约探讨

在由一个供应商和一个销售商构成的供应链系统中,构建了两种产品的捆绑式数量折扣合约数学模型,探讨了产品在市场需求为独立均匀分布和正态分布情况下销售商订货策略的解析解,并通过数值模拟验证了模型的正确性。     

基于Markov链的物流运输企业应收账款风险分析

应收账款是物流运输企业的一项重要的流动资产,其风险分析是企业资产管理的一项重要内容。引入具有吸收态的Markov链以建立相应的模型,从而对应收账款风险进行分析,得出企业应收账款能回收率以及坏账率。为企业的经营决策提供依据。     

基于对跨国公司商品市场占有率预测的博弈模型研究

根据对跨国公司的市场份额统计量的描述,结合伯特兰德博弈数学模型,利用马尔柯夫链的一些性质和伯特兰德博弈理论进行了分析。结果表明：利用该方法分析得到的跨国公司市场占有率与给定的目标期望函数能有较好的符合,适用于目前对跨国公司的市场占有率的预测研究。     

Bayesian outlier analysis in binary regression

We propose alternative approaches to analyze residuals in binary regression models based on random effect components. Our preferred model does not depend upon any tuning parameter, being completely automatic. Although the focus is mainly on accommodation of outliers, the proposed methodology is also able to detect them. Our approach consists of evaluating the posterior distribution of random effects included in the linear predictor. The evaluation of the posterior distributions of interest involves cumbersome integration, which is easily dealt with through stochastic simulation methods. We also discuss different specifications of prior distributions for the random effects. The potential of these strategies is compared in a real data set. The main finding is that the inclusion of extra variability accommodates the outliers, improving the adjustment of the model substantially, besides correctly indicating the possible outliers.     

An efficient computational approach for prior sensitivity analysis and cross‐validation

Prior sensitivity analysis and cross‐validation are important tools in Bayesian statistics. However, due to the computational expense of implementing existing methods, these techniques are rarely used. In this paper, the authors show how it is possible to use sequential Monte Carlo methods to create an efficient and automated algorithm to perform these tasks. They apply the algorithm to the computation of regularization path plots and to assess the sensitivity of the tuning parameter in g‐prior model selection. They then demonstrate the algorithm in a cross‐validation context and use it to select the shrinkage parameter in Bayesian regression. The Canadian Journal of Statistics 38:47–64; 2010 © 2010 Statistical Society of Canada