Parsimonious Estimation of the Covariance Matrix in Multinomial Probit Models

This article presents a Bayesian analysis of a multinomial probit model by building on previous work that specified priors on identified parameters. The main contribution of our article is to propose a prior on the covariance matrix of the latent utilities that permits elements of the inverse of the covariance matrix to be identically zero. This allows a parsimonious representation of the covariance matrix when such parsimony exists. The methodology is applied to both simulated and real data, and its ability to obtain more efficient estimators of the covariance matrix and regression coefficients is assessed using simulated data.     

各省区季度GDP数据质量评估

  国内外学者对我国GDP数据质量的质疑重点已从年度数据转到季度数据,从全国数据转向地方数据。本文通过设计一套较为系统且可操作性强的季度GDP评估指标体系,运用空间面板数据模型对各省区的季度GDP数据质量进行了实证检验。结果表明,整体来看,中国各省区季度GDP同各经济指标的匹配性较好,数据质量较高,并不存在明显的失真现象;从时间上来看,每年一、二季度的GDP存在一定程度的高估,而每年三、四季度的GDP则存在一定程度的低估,但是这种偏差在统计上不显著;分地区来看,尽管一半省区的季度GDP存在一定程度的高估,另一半省区存在一定程度的低估,但大部分省区高估或低估的程度在统计上不显著。文章进一步分析了其中的原因。     

First and Second Derivatives in Time Series Classification Using DTW

In our previous work, we developed a new distance function based on a derivative and showed that our algorithm is effective. In contrast to well-known measures from the literature, our approach considers the general shape of a time series rather than standard distance of function (value) comparison. The new distance was used in classification with the nearest neighbor rule. Now we improve on our previous technique by adding the second derivative. In order to provide a comprehensive comparison, we conducted a set of experiments, testing effectiveness on 47 time series datasets from a wide variety of application domains. Our experiments show that this new method provides a significantly more accurate classification on the examined datasets.     

Multivariate Control Charts Based on Hybrid Novelty Scores

We propose a new nonparametric multivariate control chart that integrates a novelty score. The proposed control chart uses as its monitoring statistic a hybrid novelty score, calculated based on the distance to local observations as well as on the distance to the convex hull constructed by its neighbors. The control limits of the proposed control chart were established based on a bootstrap method. A rigorous simulation study was conducted to examine the properties of the proposed control chart under various scenarios and compare it with existing multivariate control charts in terms of average run length (ARL) performance. The simulation results showed that the proposed control chart outperformed both the parametric and nonparametric Hotelling's T ² control charts, especially in nonnormal situations. Moreover, experimental results with real semiconductor data demonstrated the applicability and effectiveness of the proposed control chart. To increase the capability to detect small mean shift, we propose an exponentially weighted hybrid novelty score control chart. Simulation results indicated that exponentially weighted hybrid score charts outperformed the hybrid novelty score based control charts.     

Statistical Computing: History and Trends

The evolution of computers is currently in a period of rapid change, stimulated by radically cheaper and smaller devices for processing and memory. These changes are certain to provide major opportunities and challenges for the use of computers in statistics. This article looks at history and current trends, in both general computing and statistical computing, with the goal of identifying key features and requirements for the near future. A discussion of the S language developed at Bell Laboratories illustrates some program design principles that can make future work on statistical programs more effective and more valuable.     

INFLUENCE FUNCTIONS IN FUNCTIONAL MEASUREMENT ERROR MODELS WITH REPLICATED DATA

We consider the construction and properties of influence functions in the context of functional measurement error models with replicated data. In these models estimates of the parameters can be affected both by the individual observations and the means of replicated observations. We show that influence function of the means of replicates on the estimate of regression coefficients can be only derived under the assumption that the variances of the errors are known, while one for the individual observations can be only derived simultaneously with their influence function on the estimators of the variances of the errors.     

Performance evaluation based on multiple attributes with nonparametric frontiers

Performance rating and comparison of a group of entities is frequently based on the values of several attributes. Such evaluations are often complicated by the absence of a natural or obvious way to weight the importance of the individual dimensions of the performance. This paper proposes a framework based on nonparametric frontiers to rate and classify entities described by multiple performance attributes into ‘performers’ and ‘underperformers’. The method is equivalent to Data Envelopment Analysis (DEA) with entities defined only by outputs. In the spirit of DEA, the weights for each attribute are selected to maximize each entity’s performance score. This approach, however, results in a new linear program that is more direct and intuitive than traditional DEA formulations. The model can be easily understood and interpreted by practitioners since it conforms better to the practice of evaluating and comparing performance using standard specifications. We illustrate the model’s use with two examples. The first evaluates the performance of employees. The second is an application in manufacturing where multiple quality attributes are used to assess and compare performance of different manufacturing processes.     

Reconfiguring the Supply Network Using Current Performance Data

Reconfiguration of the supply chain network from time to time is essential for businesses to retain their competitive edge. This paper presents a methodology for reconfiguration of an existing supply chain network. The methodology is characterized by two decision levels. In the first level, the current network performance is evaluated and efficient practices are identified. In the next level, a model that incorporates efficient practices is developed to reconfigure the network. This integrated methodology allows for decision maker (DM) input throughout the process. The methodology has been implemented and tested in the reconfiguration of an outbound petroleum supply chain network for CountryMark Cooperative, Inc. In this case study, Data Envelopment Analysis (DEA) is used to analyze current operations and an integer programming (IP) model that incorporates efficiency metrics is developed for selection of distribution facilities and allocation of resources to the facilities. Use of this methodology can lead to improved operations and reduced operating expenses.     

中国投资者的风险偏好

 投资者如何分配家庭资产,为个体的风险偏好研究提供了重要的信息。本文采用奥尔多投资研究中心《城市投资者行为调查问卷》的调查结果,从投资者对风险资产需求这一角度,详细研究哪些因素会显著影响投资者的风险偏好,以及不同群体投资者风险偏好的异质性问题。研究结果表明：投资者的财富水平、受教育程度、健康状况、收入水平和是否抚养小孩都是影响投资者风险偏好的重要因素。同时,不同群体风险偏好的表现有差异。研究中国投资者风险偏好的异质性,可以为进一步研究投资者的金融资产决策、储蓄行为、财富积累过程以及不同群体对宏观经济政策的反应提供依据。     

统计数据项目、发布渠道与用户质量感知

 通过实施统计用户满意度调查,测量统计用户对于政府统计部门所生产各类统计数据项目的综合质量感知,及其在主要发布渠道的具体质量感知状况。基于调查数据的分析结果表明：各类统计数据项目的综合用户质量感知水平存在较为明显的差异,用户质量感知与其使用频率之间保持了一定的正相关关系;而各类统计项目在不同发布渠道的具体质量表现也不尽相同。据此可以有效确立统计数据质量管理工作的重点环节。