“批对批”策略下关于供应商管理库存机制的一个探讨

本文考虑了一个由单供应商和单采购商构成的两级供应链系统,在供应商采取"批对批"策略下,引入VMI后将对供应链系统以及各渠道成员利益带来的影响.我们发现,渠道权力配置对VMI作为供应链企业间合作机制的有效性存在重要影响.在采购商占主导地位的渠道中,当系统参数满足一定条件时,引入VMI后可以同时降低供应商和采购商总成本,从而VMI可以成为供应商和采购商之间的有效合作机制.但在供应商占主导地位的渠道中,引入VMI必将增加供应商的总成本,此时VMI不是供应商和采购商之间的有效合作机制.     

On Tiku's robust procedure — a Bayesian insight

Tiku's robust procedure for testing mean and variance from nonnormal universe is examined from the Bayesian viewpoint. The posterior distribution of the scale parameter is derived and then approximated by a Laguerre polynomial expansion while the posterior distribution of the location parameter is approximated by a linear combination of t-distributions. For the example with Darwin's data, the approximations appear to be extremely good.     

基于逆高斯分布的项目投资风险收益理论模型

项目投资经济效果评价指标的风险分析指的是如何依据各影响因素的概率分布来推求经济效果评价指标的概率分布.本文从理论上建立了基于逆高斯分布项目投资风险收益理论模型并给予精细的分析.这对提高项目投资经济的可靠性及风险分析理论具有重要意义.     

指数分布的条件

对处于冲击环境中的系统,讨论了导致系统寿命分布是负指数分布的条件,得到许多重要结果。它们是假设系统寿命分布为负指数分布的理论依据。     

CONDITIONAL INFERENCE ABOUT AN EQUICORRELATION COEFFICIENT

SenGupta (1987) proposed a locally most powerful test which is globally (one sided) unbiased, and an estimator of p, the equicorrelation coefficient of a standard symmetric multivariate normal (SSMN) distribution. Here we use the idea in Williams (1984) to illustrate the construction and use of ancillary statistics to make inference about p. The test and confidence intervals based on this construction are conditionally optimal.     

记忆的测量模型

本文讨论了正态分布下的信号检测法，对数分布下的信号检测法，双高阈限模型法和非参数模型法等四种具有代表性的记忆测量模型并对以上模型的分辩力指标和反应倾向指标作了分析说明．     

Goodness-of-fit tests for Pareto distribution based on a characterization and their asymptotics

In this paper we present a new characterization of the Pareto distribution and consider goodness-of-fit tests based on it. We provide an integral and Kolmogorov–Smirnov-type statistics based on U-statistics and we calculate Bahadur efficiency for various alternatives. We find locally optimal alternatives for those tests. For small sample sizes, we compare the power of those tests with some common goodness-of-fit tests.     

Bayesian estimation and case influence diagnostics for the zero-inflated negative binomial regression model

In recent years, there has been considerable interest in regression models based on zero-inflated distributions. These models are commonly encountered in many disciplines, such as medicine, public health, and environmental sciences, among others. The zero-inflated Poisson (ZIP) model has been typically considered for these types of problems. However, the ZIP model can fail if the non-zero counts are overdispersed in relation to the Poisson distribution, hence the zero-inflated negative binomial (ZINB) model may be more appropriate. In this paper, we present a Bayesian approach for fitting the ZINB regression model. This model considers that an observed zero may come from a point mass distribution at zero or from the negative binomial model. The likelihood function is utilized to compute not only some Bayesian model selection measures, but also to develop Bayesian case-deletion influence diagnostics based on q-divergence measures. The approach can be easily implemented using standard Bayesian software, such as WinBUGS. The performance of the proposed method is evaluated with a simulation study. Further, a real data set is analyzed, where we show that ZINB regression models seems to fit the data better than the Poisson counterpart.