基于改进得分函数的直觉模糊多属性决策方法

针对属性权重信息完全未知的直觉模糊多属性决策问题进行了探究,提出了一种综合考虑隶属度、非隶属度以及犹豫度的新的得分函数,利用改进的得分函数将传统的熵权法推广到直觉模糊领域,为属性权重完全未知的直觉模糊多属性决策问题提供了一个新的排序方法。实际的铁路冻害整治算例进一步说明该方法的可行性和有效性。     

准入制度改革、同业竞争与银行业市场进入决策机制升级

为了研究我国银行业市场准入制度改革对银行市场进入决策机制的影响和检验银行间差异化竞争战略的实施成效,本文基于中小股份制商业银行分支数据构建静态离散博弈的均衡结构模型还原银行市场进入过程中的战略互动,并重点考察不同垄断地位银行间非对称的竞争壁垒效应。鉴于多重Nash均衡引起的估计不一致问题,分别使用条件矩不等式和子样本模拟技术识别与估计模型参数的置信区间,并进一步设计反事实分析和分类比较分析。研究发现,我国银行业准入制度放松显著推动了市场进入决策机制升级,使其不仅取决于银行内部优势和市场区位,也受制于对手决策及其构建的品牌、规模经济等非对称竞争壁垒;银行边际利润实现从第二产业向第三产业转移,当前银行业的“壁龛市场”战略占优于“跟随”战略。     

A LOTTO SYSTEMS PROBLEM

A Lotto Systems bet allows the player to nominate n numbers from which (s)he believes the winning six numbers will be drawn, and to bet on all combinations of six of these n numbers. Assume that the winning six numbers come from the nominated n. How many combinations must be entered to guarantee that one combination will include at least five of the winning six numbers? The problem is generalized in this paper, and the method of simulated annealing is used to find solutions for various situations. The case where two supplementary numbers are drawn after the initial six winning numbers is also considered.     

Expert Knowledge Elicitation: Subjective but Scientific

ABSTRACT

Expert opinion and judgment enter into the practice of statistical inference and decision-making in numerous ways. Indeed, there is essentially no aspect of scientific investigation in which judgment is not required. Judgment is necessarily subjective, but should be made as carefully, as objectively, and as scientifically as possible.

Elicitation of expert knowledge concerning an uncertain quantity expresses that knowledge in the form of a (subjective) probability distribution for the quantity. Such distributions play an important role in statistical inference (for example as prior distributions in a Bayesian analysis) and in evidence-based decision-making (for example as expressions of uncertainty regarding inputs to a decision model). This article sets out a number of practices through which elicitation can be made as rigorous and scientific as possible.

One such practice is to follow a recognized protocol that is designed to address and minimize the cognitive biases that experts are prone to when making probabilistic judgments. We review the leading protocols in the field, and contrast their different approaches to dealing with these biases through the medium of a detailed case study employing the SHELF protocol.

The article ends with discussion of how to elicit a joint probability distribution for multiple uncertain quantities, which is a challenge for all the leading protocols. Supplementary materials for this article are available online.     

Convolution of fuzzy distributions in decision-making

This paper deals with a decision-making problem that has hardly been addressed so far. Frequently, management is challenged to deal with sums of random variables, the distribution of which is not fully known. For example, a high return on an investment project may be “more likely” than a low one in the first period but “less likely” in the second period; however, the investment decision has to be based on the sum of the two retums. The paper contains some essential theorems concerning the convolution of distribution functions for the case of fuzzy random variables. Moreover, an example is given illustrating the incorporation of new Linear Partial Information (LPI) and the transition from an a priori to a posteriori convolution of fuzzy distribution functions.     

Design of a sign chart using a new EWMA statistic

Abstract

In this article, a new non parametric control chart based on the modified or controlled exponentially weighted moving average (EWMA) statistic is developed to monitor the process deviation from the target value. The proposed control chart is evaluated for different values of design parameters using the average run length as a performance criterion under various sample sizes. The proposed chart is compared with the existing non parametric EWMA sign control chart. It is observed that the proposed chart is better than the existing EWMA sign control chart in terms of run length characteristics. An empirical example is provided for the practical implementation of the proposed chart.     

A dynamic analysis of stock markets using a hidden Markov model

This paper proposes a framework to detect financial crises, pinpoint the end of a crisis in stock markets and support investment decision-making processes. This proposal is based on a hidden Markov model (HMM) and allows for a specific focus on conditional mean returns. By analysing weekly changes in the US stock market indexes over a period of 20 years, this study obtains an accurate detection of stable and turmoil periods and a probabilistic measure of switching between different stock market conditions. The results contribute to the discussion of the capabilities of Markov-switching models of analysing stock market behaviour. In particular, we find evidence that HMM outperforms threshold GARCH model with Student-t innovations both in-sample and out-of-sample, giving financial operators some appealing investment strategies.     

基于可拓学的开发区项目准入模型

设计开发区项目准入评价的指标体系,运用可拓理论建立项目准入决策的综合评价模型,通过实例演示该模型应用的具体步骤,为开发区招商项目准入评价提供了一种科学可行的方法,从而实现了土地集约化前提下开发区项目引入的优化决策。     

Multivariate Life Testing in Variably Scaled Environments

This paper examines modeling and inference questions for experiments in which different subsets of a set of k possibly dependent components are tested in r different environments. In each environment, the failure times of the set of components on test is assumed to be governed by a particular type of multivariate exponential (MVE) distribution. For any given component tested in several environments, it is assumed that its marginal failure rate varies from one environment to another via a change of scale between the environments, resulting in a joint MVE model which links in a natural way the applicable MVE distributions describing component behavior in each fixed environment. This study thus extends the work of Proschan and Sullo (1976) to multiple environments and the work of Kvam and Samaniego (1993) to dependent data. The problem of estimating model parameters via the method of maximum likelihood is examined in detail. First, necessary and sufficient conditions for the identifiability of model parameters are established. We then treat the derivation of the MLE via a numerically-augmented application of the EM algorithm. The feasibility of the estimation method is demonstrated in an example in which the likelihood ratio test of the hypothesis of equal component failure rates within any given environment is carried out.     

The use of utilities and experts in decision-making

In the process of decision-making the decision-maker may be able to consult an expert or experts or there may be no experts available. Four different situations that the decision-maker may be confronted with, are considered. The utilities of the decision-maker are used in this decision-making process to obtain his Bayes decision. The four different situations are illustrated by an example.     

A note on testing the regression functions via nonparametric smoothing

The authors present a consistent lack‐of‐fit test in nonlinear regression models. The proposed procedure possesses some nice properties of Zheng's test such as the consistency, the ability to detect any local alternatives approaching the null at rates slower than the parametric rate. What's more, for a predetermined kernel function, the proposed test is more powerful than Zheng's test and the validity of these findings is confirmed by the simulation studies and a real data example. In addition, the authors find out a close connection between the choices of normal kernel functions and the bandwidths. The Canadian Journal of Statistics 39: 108–125; 2011 © 2011 Statistical Society of Canada     

Replenishing the pipeline: A quantitative approach to optimising the sourcing of new projects

Large pharmaceutical companies maintain a portfolio of assets, some of which are projects under development while others are on the market and generating revenue. The budget allocated to R&D may not always be sufficient to fund all the available projects for development. Much attention has been paid to the selection of optimal subsets of available projects to fit within the available budget. In this paper, we argue the need for a forward-looking approach to portfolio decision-making. We develop a quantitative model that allows the portfolio management to evaluate the need for future inflow of new projects to achieve revenue at desired levels, often aspiring to a certain annual revenue growth. Optimisation methods are developed for the presented model, allowing an optimal choice of number, timing and type of projects to be added to the portfolio. The proposed methodology allows for a proactive approach to portfolio management, prioritisation, and optimisation. It provides a quantitatively based support for strategic decisions regarding the efforts needed to secure the future development pipeline and revenue stream of the company.     

Rapid detection of the switching point in a financial market structure using the particle filter

We apply the particle filter for the quick and accurate estimation of a switching point in a financial market based on a recently developed theoretical model, the potentials of unbalanced complex kinetics (PUCK) model, which fulfils all empirically stylized facts such as fat-tailed distribution of price changes and the anomalous diffusion in a short-time scale. We show the efficiency of an optimized driving force in particle filtering for the estimation of the parameters of the PUCK model, using a simulation study. As an example, we apply the method to the dollar–yen exchange market before and after the biggest earthquake in Japan in March 2011. With this fast and efficient estimation method, we can clearly confirm that the statistics of the time series of exchange rate changed drastically at the time of the arrival of the quake in Tokyo area, implying that the earthquake worked as a trigger for the market's switching point.     

Supplier Evaluation and Selection Based on Stochastic Dominance: A Quality-Based Approach

Quality has become a major business strategy such that organizations with successful improvement of their products quality can gain productivity, enhance market penetration, achieve great profitability, and strongly sustain their competitive advantages. The quality of materials received from suppliers determines not only the quality of assembled products but also satisfaction and loyalty of downstream customers. In this article, we employ decision-making processes of the stochastic dominance on the basis of loss-based capability indices to compare certain potential suppliers. In view of compared results of the first-order and second-order stochastic dominances, each supplier is categorized as a superior supplier, weakly superior supplier, strongly non dominated supplier, or non dominated supplier. We develop a general computational procedure to select the preferable suppliers in an analytical way. To assist decision-makers in selecting preferable suppliers, quantile-quantile plots of loss-based capability indices presenting the results of the first-order stochastic dominance of the indices’ estimators are developed so that they can simultaneously visualize pair-wise comparisons of the suppliers and make appropriate decisions. Finally, a practical example invoking the stochastic dominance using the loss-based capability indices to carry out the quality-based supplier evaluation and selection is presented to demonstrate the applicability of our proposed methodology.     

Expansion estimation by Bayes rules

In the problem of estimating a location parameter in any symmetric unimodal location parameter model, we demonstrate that Bayes rules with respect to squared error loss can be expanders for some priors that belong to the family of all symmetric priors. That generalizes the results obtained by DasGupta and Rubin for the one dimensional case. We also consider symmetric priors which either have an appropriate point mass at 0 or are unimodal, and prove that under the latter condition all Bayes rules are shrinkers. Results of such nature are important, for example, in wavelet based function estimation and data denoising, where shrinkage of wavelet coefficients is associated with smoothing the data. We illustrate the results using FIAT stock market data.     

《企业经济统计学》理论体系改革方向的探讨

 随着社会主义市场经济体制的确立与完善,我国企业的经济活动也在发生着重大的变化。作为研究企业数量规律性的企业经济统计学必须适应这些变化,不断发展和改革现有的理论体系,才能更好的为企业决策和国家宏观调控服务。     

On a threshold autoregression with conditional heteroscedastic variances

This paper considers a time series model with a piecewise linear conditional mean and a piecewise linear conditional variance which is a natural extension of Tong's threshold autoregressive model. The model has potential applications in modelling asymmetric behaviour in volatility in the financial market. Conditions for stationarity and ergodicity are derived. Asymptotic properties of the maximum likelihood estimator and two model diagnostic checking statistics are also presented. An illustrative example based on the Hong Kong Hang Seng index is also reported.     

CPI的发布对中国股票市场收益率影响的实证研究——以上海股票市场为例

在引起股票市场波动的众多因素中，消费价格指数CPI日渐引起人们的关注。本文采用事件研究法，以上海证券市场为例。重点研究CPI这一宏观经济信息的公布对中国股票市场的短期影响效应。结果发现，在CPI公布前较短的时间内，股票市场出现显著的异常收益，这表明中国股市有效性不足，重要的经济信息发布前可能存在泄露问题．事后股市对信息的消化吸收也相对缓慢。在稍长的事件期内，CPI信息的发布对股市的累积公告效应较小．市场主体预期的调整和多空双方力量的对比抵消了各事件日内股市受到的信息的冲击。     

上海股票市场分形特征的实证研究

以有效市场假说为基础的现代资本市场理论被越来越多的实践证明与现实情况不符,而分形理论则考虑到资本市场的复杂性和EMH的缺陷,以非线性范式为分析基础,解释了有效市场理论无法解释的许多市场现象,为更深入地分析资本市场提供了新的思路和方法。文章以上海股票市场为例,用分形理论对上海股市的分形特征进行了研究,其结果表明上海股票市场具有明显的分形特征。     

Hierarchically penalized quantile regression

In many regression problems, predictors are naturally grouped. For example, when a set of dummy variables is used to represent categorical variables, or a set of basis functions of continuous variables is included in the predictor set, it is important to carry out a feature selection both at the group level and at individual variable levels within the group simultaneously. To incorporate the group and variables within-group information into a regularized model fitting, several regularization methods have been developed, including the Cox regression and the conditional mean regression. Complementary to earlier works, the simultaneous group and within-group variables selection method is examined in quantile regression. We propose a hierarchically penalized quantile regression, and show that the hierarchical penalty possesses the oracle property in quantile regression, as well as in the Cox regression. The proposed method is evaluated through simulation studies and a real data application.