Optimal scaling of Metropolis algorithms: Heading toward general target distributions

The authors provide an overview of optimal scaling results for the Metropolis algorithm with Gaussian proposal distribution. They address in more depth the case of high‐dimensional target distributions formed of independent, but not identically distributed components. They attempt to give an intuitive explanation as to why the well‐known optimal acceptance rate of 0.234 is not always suitable. They show how to find the asymptotically optimal acceptance rate when needed, and they explain why it is sometimes necessary to turn to inhomogeneous proposal distributions. Their results are illustrated with a simple example.     

A Rejoinder to Forman on AHP,with Emphasis on the Requirements of Composite Ratio Scales*

In an earlier paper [11], the problems of rank reversals and invalid composite priorities in AHP were addressed by modifications to the AHP procedure. That solution was subsequently criticized [5]. In this paper, we rebut these criticisms, and we show how rank reversals in AHP can arise merely from the process of normalizing local priorities.     

PREFERENCE MAPPING OF PERCEIVED INFORMATION STRUCTURE: IMPLICATIONS FOR DECISION SUPPORT SYSTEMS DESIGN

In a recent issue of this journal, Watkins [13] presented an approach for discovery of decision-maker perceptions of the complexity (dimensionality) of information items that might be supplied by a decision support system. Through use of multidimensional scaling and cluster analysis, relatively homogeneous groups of decision makers, sharing common perceptions of various information items, were formed. This prior research was referred to as a first step in suggesting that information reports could be tailored to groups of decision makers classified on the basis of common perceptions of information. The current research extends the prior study by evaluating decision maker preferences for information in a variety of decision-making scenarios in relation to the previously identified perceptions of the information. Based on the results of the study, conclusions are made which suggest that the tailoring of information to groups of decision makers should be based on both perceptions and preferences for information. Even so, it is demonstrated that the decision tasks have an impact on the preferences for information which may affect the attempt to tailor information to groups of decision makers.     

Some Cautions on the Measurement of User Information Satisfaction*

User information satisfaction (UIS) is important because of its potential effects on MIS department goals, quality of user work life, and extent of voluntary usage of systems. Reliable measurement of UIS is important for providing evaluative information for both researchers and practitioners. This study used 92 managers and executives as subjects to compare the test/retest reliability of a widely used, 13-scale UIS instrument together with four summary questions under experimental and control conditions. The summary questions behaved more reliably than the detailed questions for all groups, perhaps because of problems with scale units and origins and with item heterogeneity. This suggests that researchers need more reliable measures of UIS and that practitioners need to exercise caution when collecting and interpreting UIS scores.     

Ambiguous Criteria Weights in AHP: Consequences and Solutions*

Two related streams of criticism of the analytic hierarchy process (AHP) have not yet been satisfactorily resolved, although both date from the early 1980s. The first relates to ambiguity in the meaning of the relative importance of one criterion as compared to another. The second is concerned with reversals of rank alleged to be possible when new options are introduced in an AHP problem. Both proponents and critics of AHP agree that rank reversals occur, but disagree on the legitimacy of such reversals. This paper shows that there is a necessary correspondence between the manner in which criteria importances are interpreted and computed and the manner in which the weights of the options under each criterion are normalized. In general, if this relationship is ignored, incorrect weights are generated for options under consideration regardless of whether new options are added or deleted. A rank reversal on the addition of an option is merely symptomatic of this fact, and such reversals do not occur when the correspondence condition is met.     

什么是学术评价的全球标准——基于四个全球大学排行榜的实证分析

基于2013—2016年三大全球大学排行榜的数据,运用多维标度分析、聚类分析等技术,分析了世界一流大学的类型、特征和发展趋势。研究发现,世界一流大学可以分成两个大类和四个亚类,分别代表了不同的办学类型;在发展趋势上,呈现出\     

圆光栅动态检测的填脉冲比相法及其数据处理

本文介绍的填脉冲比相法是应用计算机技术进行圆光栅动态检测的一种新方法。由于直接用数字量进行采样和数据处理,因此解决了以前用模拟信号进行采样所产生的高频成分损失和人工定标误差这两个问题。     

Exploring the efficiency of Mexican universities: Integrating Data Envelopment Analysis and Multidimensional Scaling

National policy initiatives require the expenditure of large amounts of resources over several years. It is common for these initiatives to generate large amounts of data that are needed in order to assess their success. Educational policies are an obvious example. Here we concentrate on Mexico׳s “Educational Modernisation Programme” and try to see how this plan has affected efficiency in teaching and research at Mexico׳s universities. We use a combined approach that includes traditional ratios together with Data Envelopment Analysis models. This mixture allows us to assess changes in efficiency at each individual university and explore if these changes are related to teaching, to research, or to both. Using official statistics for 55 universities over a six year period (2007–2012), we have generated 12 ratios and estimated 21 DEA models under different definitions of efficiency. In order to make the results of the analysis accessible to the non-specialist we use models that visualise the main characteristics of the data, in particular scaling models of multivariate statistical analysis. Scaling models highlight the important aspects of the information contained in the data. Because the data is three-way (variables, universities, and years) we have chosen the Individual Differences Scaling model of Carroll and Chang. We complete the paper with a discussion of efficiency evolution in three universities.     

Scaling bias in pooled exponential random graph models

Researchers often use pooled exponential random graph models (ERGM) to analyze samples of networks. However, pooled ERGM—here, understood to include both meta-regression and combined estimation on a stacked adjacency matrix—may be biased if there is heterogeneity in the latent error variance (‘scaling’) of each lower-level model. This study explores the implications of scaling for pooled ERGM analysis. We illustrate that scaling can produce bias in pooled ERGM coefficients that is more severe than in single-network ERGM and we introduce two methods for reducing this bias. Simulations suggest that scaling bias can be large enough to alter conclusions about pooled ERGM coefficient size, significance, and direction, but can be substantially reduced by estimating the marginal effect within a block diagonal or random effects meta-regression framework. We illustrate each method in an empirical example using Add Health data on 15 in-school friendship networks. Results from the application illustrate that many substantive conclusions vary depending on choice of pooling method and interpretational quantity.     

Handling incomplete Quality-of-Life Data

Incomplete data sets are often encountered in theanalysis of quality-of-life (QOL) data. The incompleteness arisesfrom two major sources, namely, missing responses and artificialquantification of response categories. Shen and Lai (1998a)propose using Optimal Scaling (OS) to tackle the problem. The OSmethod based on numerical iterative approach attempts to restorethe continuous property of the measurements and provide estimatesfor missing responses. However, the OS leads to convergenceproblem when there are many missing values in the data set; andit incorporates no mechanisms to provide the standard errors ofthe mean estimates when missing values are filled. Hot-deckimputation is therefore suggested. This paper presents asimulation study to show that the random hot-deck imputationyields reasonable estimates for the population mean and generallypreserves the distribution of the population. In addition, whenapplying the random hot-deck imputation, valid estimates for thestandard error of the mean estimate can be obtained using thevariance formula due to Lai (1998). With hot-deck imputationtaking care of the missing responses and OS quantifying theresponse categories, it is postulated that the problem of dataincompleteness can be more satisfactorily handled. By applyingthe proposed techniques to real survey data, this paper alsopresents the change of the QOL of Hong Kong residents in the lastdecade leading to the turning point of the metropolis in 1997.