基于混合加权距离的毕达哥拉斯模糊TOPSIS多属性决策方法研究

针对决策信息为毕达哥拉斯模糊集的多属性决策问题，提出了一种基于混合加权测度的TOPSIS决策方法。在分析了现有距离测度方法不足的基础上，首先给出了一种新的毕达哥拉斯模糊距离测度——毕达哥拉斯模糊有序加权距离（PFOWD），并研究了该测度权重的确定方法；在PFOWD基础上，进一步提出了毕达哥拉斯模糊混合加权距离（PFHWD），同时探讨了其特征和与现有毕达哥拉斯模糊测度的关系；最后提出了一种基于PFHWD测度的毕达哥拉斯模糊TOPSIS多属性方法，并用实例验证了方法的有效性和可行性。     

Non-ignorable missing covariate data in survival analysis: a case-study of an International Breast Cancer Study Group trial

Summary.  Non-ignorable missing data, a serious problem in both clinical trials and observational studies, can lead to biased inferences. Quality-of-life measures have become increasingly popular in clinical trials. However, these measures are often incompletely observed, and investigators may suspect that missing quality-of-life data are likely to be non-ignorable. Although several recent references have addressed missing covariates in survival analysis, they all required the assumption that missingness is at random or that all covariates are discrete. We present a method for estimating the parameters in the Cox proportional hazards model when missing covariates may be non-ignorable and continuous or discrete. Our method is useful in reducing the bias and improving efficiency in the presence of missing data. The methodology clearly specifies assumptions about the missing data mechanism and, through sensitivity analysis, helps investigators to understand the potential effect of missing data on study results.     

Factor analysis for ranked data with application to a job selection attitude survey

Summary.  Factor analysis is a powerful tool to identify the common characteristics among a set of variables that are measured on a continuous scale. In the context of factor analysis for non-continuous-type data, most applications are restricted to item response data only. We extend the factor model to accommodate ranked data. The Monte Carlo expectation–maximization algorithm is used for parameter estimation at which the E-step is implemented via the Gibbs sampler. An analysis based on both complete and incomplete ranked data (e.g. rank the top q out of k items) is considered. Estimation of the factor scores is also discussed. The method proposed is applied to analyse a set of incomplete ranked data that were obtained from a survey that was carried out in GuangZhou, a major city in mainland China, to investigate the factors affecting people's attitude towards choosing jobs.     

Random variate transformations in the Gibbs sampler: issues of efficiency and convergence

In the non-conjugate Gibbs sampler, the required sampling from the full conditional densities needs the adoption of black-box sampling methods. Recent suggestions include rejection sampling, adaptive rejection sampling, generalized ratio of uniforms, and the Griddy-Gibbs sampler. This paper describes a general idea based on variate transformations which can be tailored in all the above methods and increase the Gibbs sampler efficiency. Moreover, a simple technique to assess convergence is suggested and illustrative examples are presented.     

Bayesian analysis of outlier problems using divergence measures

A Bayesian approach is presented for detecting influential observations using general divergence measures on the posterior distributions. A sampling-based approach using a Gibbs or Metropolis-within-Gibbs method is used to compute the posterior divergence measures. Four specific measures are proposed, which convey the effects of a single observation or covariate on the posterior. The technique is applied to a generalized linear model with binary response data, an overdispersed model and a nonlinear model. An asymptotic approximation using Laplace method to obtain the posterior divergence is also briefly discussed.     

基于MCMC稳态模拟的贝叶斯经验费率厘定信用模型

B黨lmann-Straub model is one of the most famous applications of the Bayesian method for the experience rate making.However,by the traditional B黨lmann-Straub model one cannot get the unbiased posterior estimation of the parameters when there is not sufficient prior information for the structural parameters;What's more,the difficult of computing high dimension numeration limits the application of Bayesian method.This paper introduces the Markov chain Monte Carlo simulaton method based on the Gibbs sampling after analyzing the structure of the B黨lmann-Straub model and sets up the Bayesian credibility model for estimating the predictive risk premium.Also by using the results of the numeration analysis,this paper proves that from this model one can get the posterior distributions of the parameters dynamically and the posterior estimation of the censoring parameters in the situation that exists unknown parameters,as well as improve the precision of the numeration,which can be helpful to find the heterogeneity of the premium.     

基于混合智能算法的电子市场供应链期权合同协调

在电子市场环境下,考虑了需求、市场价格和市场准入程度的随机性,基于Stack-erlberg主从对策,建立了供应链期权合同协调的随机期望值模型。在这一主从对策模型中,主方供应商的目标函数是预期利润,决策变量是期权合同预订费用和执行费用;从方分销商的目标函数是预期利润,决策变量是订货量。应用包括随机模拟、人工神经元网络和遗传算法组成的混合智能算法求解该主从对策问题。最后,结合上海宝钢集团益昌公司电子商务的运作实例,运用混合智能算法进行了仿真计算与分析。     

Nonparametric Bayesian inference for multivariate density functions using Feller priors

Multivariate density estimation plays an important role in investigating the mechanism of high-dimensional data. This article describes a nonparametric Bayesian approach to the estimation of multivariate densities. A general procedure is proposed for constructing Feller priors for multivariate densities and their theoretical properties as nonparametric priors are established. A blocked Gibbs sampling algorithm is devised to sample from the posterior of the multivariate density. A simulation study is conducted to evaluate the performance of the procedure.     

Nonprofit organizations in between the nonprofit and market spheres: Shifting goals,governance and management?

In spite of the belief instilled by the New Public Management reforms that nonprofit organizations (NPOs) can benefit from more management, more measurement and more market practices, systematic knowledge on the organizational effects of NPOs incorporating business practices in their day‐to‐day functioning remains absent to date. This research note addresses this limitation by reviewing 49 research articles. The focus lies on the redefinition of nonprofits' mission and income streams, changing governance arrangements and shifting management practices. We find that, despite numerous detrimental effects cited in the literature, (a) generating commercial income can contribute to the financial stability of NPOs, and (b) hybridization towards the market domain can strengthen the organizational legitimacy of NPOs, suggesting that imitating for‐profit enterprises might contribute to nonprofit functioning in perception, rather than in practice.     

Bias correction for parameter estimates of spatial point process models

When a spatial point process model is fitted to spatial point pattern data using standard software, the parameter estimates are typically biased. Contrary to folklore, the bias does not reflect weaknesses of the underlying mathematical methods, but is mainly due to the effects of discretization of the spatial domain. We investigate two approaches to correcting the bias: a Newton–Raphson-type correction and Richardson extrapolation. In simulation experiments, Richardson extrapolation performs best.