A Note on the Power of Multiattribute One‐switch Utility Functions

One‐switch utility functions model situations in which the preference between two alternatives switches only once as the outcome of one attribute of both alternatives changes from low to high. Recent research cites evidence that the sum of exponential functions (sumex) is the most convincing type for modelling one‐switch utility functions. Sumex functions allow to model exactly one preferential switch and they are convenient for estimating one‐switch utility functions. However, it is unclear so far if sumex functions are suitable to model preferential switches that are perceivable by a decision maker. This paper first analyses how different the utility of two alternatives before and after a preferential can be modelled with sumex functions given that the preferential switch is caused by a particular attribute outcome improvement. It thereafter investigates how accurately decision makers perceive such utility differences. Copyright © 2015 John Wiley & Sons, Ltd.     

Rough approximation of pairwise comparisons described by multi‐attribute stochastic dominance

Given a finite set A of actions evaluated by a set of attributes, preferential information is considered in the form of a pairwise comparison table including pairs of actions from subset B⊂A described by stochastic dominance relations on particular attributes and a total order on the decision attribute. Using a rough sets approach for the analysis of the subset of preference relations, a set of decision rules is obtained, and these are applied to a set A\B of potential actions. The rough sets approach of looking for the reduction of the set of attributes gives us the possibility of operating on a multi‐attribute stochastic dominance for a reduced number of attributes. Copyright © 1999 John Wiley & Sons, Ltd.     

Attribute weighting methods and decision quality in the presence of response error: a simulation study

This paper uses a simulation approach to investigate how different attribute weighting techniques affect the quality of decisions based on multiattribute value models. The weighting methods considered include equal weighting of all attributes, two methods for using judgments about the rank ordering of weights, and a method for using judgments about the ratios of weights. The question addressed is: How well does each method perform when based on judgments of attribute weights that are unbiased but subject to random error? To address this question, we employ simulation methods. The simulation results indicate that ratio weights were either better than rank order weights (when error in the ratio weights was small or moderate) or tied with them (when error was large). Both ratio weights and rank order weights were substantially superior to the equal weights method in all cases studied. Our findings suggest that it will usually be worth the extra time and effort required to assess ratio weights. In cases where the extra time or effort required is too great, rank order weights will usually give a good approximation to the true weights. Comparisons of the two rank-order weighting methods favored the rank-order-centroid method over the rank-sum method. © 1998 John Wiley & Sons, Ltd.     

How Do Propensity Score Methods Measure Up in the Presence of Measurement Error? A Monte Carlo Study

Considering that the absence of measurement error in research is a rare phenomenon and its effects can be dramatic, we examine the impact of measurement error on propensity score (PS) analysis used to minimize selection bias in behavioral and social observational studies. A Monte Carlo study was conducted to explore the effects of measurement error on the treatment effect and balance estimates in PS analysis across seven different PS conditioning methods. In general, the results indicate that even low levels of measurement error in the covariates lead to substantial bias in estimates of treatment effects and concomitant reduction in confidence interval coverage across all methods of conditioning on the PS.     

Multi‐Criteria Evaluation for Collaborative Circulating Farming with Collective Operations Between Arable and Cattle Farmers

Tokachi sub‐prefecture in Hokkaido is one of the most famous dairy and crop farming regions in Japan. It is known that Tokachi is faced with various difficult problems such as soil degradation, water contamination and unpleasant odours because of the excessive use of chemical fertilizers and inappropriate treatment of livestock excretion. In this paper, we focus on Shihoro town where agricultural outputs are relatively large in Tokachi, and propose collaborative circulating farming with collective operations between arable and cattle farmers. Under the assumption that the decision‐maker in this problem is a representative of a farming organization who hopes for sustainable agricultural development and values the intentions of local residents including arable and cattle farmers in this region, we employ multi‐attribute utility theory in order to evaluate multiple alternatives of the farming management problem. Copyright © 2012 John Wiley & Sons, Ltd.     

含题组的测验等值

题组越来越多地出现在各类考试中, 采用标准的IRT模型对有题组的测验等值, 可能因忽略题组的局部相依性导致等值结果的失真。为解决此问题, 我们采用基于题组的2PTM模型及IRT特征曲线法等值, 以等值系数估计值的误差大小作为衡量标准, 以Wilcoxon符号秩检验为依据, 在几种不同情况下进行了大量的Monte Carlo模拟实验。实验结果表明, 考虑了局部相依性的题组模型2PTM绝大部分情况下都比2PLM等值的误差小且有显著性差异。另外, 用6种不同等值准则对2PTM等值并评价了不同条件下等值准则之间的优劣。     

缺失数据的结构方程建模:全息极大似然估计时辅助变量的作用

本研究通过蒙特卡洛模拟考查了采用全息极大似然估计进行缺失数据建模时辅助变量的作用。具体考查了辅助变量与研究变量的共缺机制、共缺率、相关程度、辅助变量数目与样本量等因素对参数估计结果精确性的影响。结果表明,当辅助与研究变量共缺时:(1)对于完全随机缺失的辅助变量,结果更容易出现偏差;(2)对于MAR-MAR组合机制,纳入单个辅助变量是有益的;对于MAR-MCAR或MAR-MNAR组合机制,纳入多于一个辅助变量的效果更好;(3)纳入与研究变量低相关的辅助变量对结果也是有益的。     

中介效应的点估计和区间估计:乘积分布法、非参数Bootstrap和MCMC法

针对中介效应ab的抽样分布往往不是正态分布的问题,学者近年提出了三类无需对ab的抽样分布进行任何限制且适用于中、小样本的方法,包括乘积分布法、非参数Bootstrap和马尔科夫链蒙特卡罗(MCMC)方法.采用模拟技术比较了三类方法在中介效应分析中的表现.结果发现:1)有先验信息的MCMC方法的ab点估计最准确;2)有先验信息的MCMC方法的统计功效最高,但付出了低估第Ⅰ类错误率的代价,偏差校正的非参数百分位Bootstrap方法的统计功效其次,但付出了高估第Ⅰ类错误率的代价;3)有先验信息的MCMC方法的中介效应区间估计最准确.结果表明,当有先验信息时,推荐使用有先验信息的MCMC方法;当先验信息不可得时,推荐使用偏差校正的非参数百分位Bootstrap方法.     

EM and beyond

The basic theme of the EM algorithm, to repeatedly use complete-data methods to solve incomplete data problems, is also a theme of several more recent statistical techniques. These techniques—multiple imputation, data augmentation, stochastic relaxation, and sampling importance resampling—combine simulation techniques with complete-data methods to attack problems that are difficult or impossible for EM.A preliminary version of this article was the Keynote Address at the 1987 European Meeting of the Psychometric Society June 24–26, 1987 in Enschede, The Netherlands. The author wishes to thank the editor and reviewers for helpful comments.