A Multiplicative Alo-group Based Hierarchical Decision Model and Application

Pairwise comparison matrix (PCM) is a popular technique used in multi-criteria decision making. The abelian linearly ordered group (alo-group) is a powerful tool for the discussion of PCMs. In this article, a criterion for acceptable consistency of PCM is introduced, which is independent of the scale and can be intuitively interpreted. The relation of the introduced criterion with the weak consistency is investigated. Then, a multiplicative alo-group based hierarchical decision model is proposed. The following approaches are included: (1) the introduced criterion for acceptable consistency is used to check whether or not a PCM is acceptable; (2) the row’s geometric mean method is used for deriving the local priorities of a multiplicative PCM; (3) a Hierarchy Composition Rule derived from the weighted mean is used for computing the criterion/subcriterion’s weights with regard to the total goal; and (4) the weighted geometric mean is used as the aggregation rule, where the alternative’s local priorities are min-normalized. The proposed model has the property of preserving rank. Moreover, it has counterparts in the additive case. Finally, the model is applied to a layout planning problem of an aircraft maintenance base with a computer-based software.     

Robust group non-convex estimations for high-dimensional partially linear models

High-dimensional data with a group structure of variables arise always in many contemporary statistical modelling problems. Heavy-tailed errors or outliers in the response often exist in these data. We consider robust group selection for partially linear models when the number of covariates can be larger than the sample size. The non-convex penalty function is applied to achieve both goals of variable selection and estimation in the linear part simultaneously, and we use polynomial splines to estimate the nonparametric component. Under regular conditions, we show that the robust estimator enjoys the oracle property. Simulation studies demonstrate the performance of the proposed method with samples of moderate size. The analysis of a real example illustrates that our method works well.     

Increasing the sample size at interim for a two‐sample experiment without Type I error inflation

For the case of a one‐sample experiment with known variance σ²=1, it has been shown that at interim analysis the sample size (SS) may be increased by any arbitrary amount provided: (1) The conditional power (CP) at interim is ?50% and (2) there can be no decision to decrease the SS (stop the trial early). In this paper we verify this result for the case of a two‐sample experiment with proportional SS in the treatment groups and an arbitrary common variance. Numerous authors have presented the formula for the CP at interim for a two‐sample test with equal SS in the treatment groups and an arbitrary common variance, for both the one‐ and two‐sided hypothesis tests. In this paper we derive the corresponding formula for the case of unequal, but proportional SS in the treatment groups for both one‐sided superiority and two‐sided hypothesis tests. Finally, we present an SAS macro for doing this calculation and provide a worked out hypothetical example. In discussion we note that this type of trial design trades the ability to stop early (for lack of efficacy) for the elimination of the Type I error penalty. The loss of early stopping requires that such a design employs a data monitoring committee, blinding of the sponsor to the interim calculations, and pre‐planning of how much and under what conditions to increase the SS and that this all be formally written into an interim analysis plan before the start of the study. Copyright © 2009 John Wiley & Sons, Ltd.     

On screening samples in the laboratory and factors in factorial investigations

Procedures such as group testing and fractional replication can greatly reduce the workload. The former is especially efficient when the presence of a characteristic in samples is low. Fractional replication can greatly reduce the amount of experimentation when some of the parameters associated with a factorial are nonexistent or negligible. Supersaturated fractional replication can be used to efficiently screen factors when it is suspected that they may not affect a process but it is desired to check this suspicion. There are many types of grouping and pooling material in an investigation. Some of these are discussed as are the interrelationships among the above procedures.     

关于n元置换群的一个同构群的注记

本文用群的观点研究置换矩阵的性质 ,并在n阶置换矩阵与n阶置换群之间建立了同构关系。特别指明在此同构关系中两个群内的二元运算是相互反序的     

An Axiomatization of the Serial Cost‐Sharing Method

We offer an axiomatization of the serial cost‐sharing method of Friedman and Moulin (1999). The key property in our axiom system is Group Demand Monotonicity, asking that when a group of agents raise their demands, not all of them should pay less.     

一种评价信息不完全的混合型多属性群决策方法

针对一类评价信息不完全且属性值由精确数、语言评价值等定量、定性形式构成的混合型不完全信息多属性群决策问题,提出了一种基于直觉模糊集和证据理论的决策方法。首先定义了转换函数将不同类型属性值一致转换为更符合决策实际的直觉模糊数,接着根据信息熵原理利用直觉模糊数中的犹豫度确定专家客观权重,随后应用证据理论集结含有信息缺失的评价值,并构造了一可能度比较公式用于集结后方案排序。该法解决了决策问题中易出现的信息残缺和混合型信息等情况,具有更强的实用性。最后的实例分析证明了该方法的科学性、有效性。     

Preferences in teams and hierarchies

This paper describes the methodology developed for a laboratory for project selection and the results of use of the computerized ranking procedure for other complex managerial decisions. In addition to project selection, the system is feasible and instructive in personnel selection, comparison of management strategies, purchasing decisions and merit-based distribution of awards. The massive input required by many existing systems is not necessary for this absolute deviation ranking system. Up to 20 attributes and 200 projects can be compared. Measures of consistency and reliability arc proposed for estimating the validity of the final weights. Preliminary experiments and actual use of the system provide empirical validation. The system offers an organized, structured way of making decisions which enhances the identification and subsequent refinement of criteria considered, and provides a concise, highly credible format for presentation and justification of decisions.     

Participatory analysis of accidents and incidents as a tool for increasing safety behaviour in fishermen. A pilot intervention study

Abstract

Although occupational accidents are common in fishery, safety work is often not given priority by the fishermen. The aims of this study were to test a group-based intervention for increased activity in safety work through group discussion of accident/incident experience; to study occurred incidents/accidents and how such events were managed; and to study intervention effects on activity in safety work, risk acceptance and perceived manageability of risks. A sample of men from five crews (11 men) of Swedish fishermen participated. The study had a one group pre-test–post-test design for questionnaire data. The emphasis was on qualitative information collected during the intervention and interviews. The results indicated that accident causes could be appraised as being unmanageable even when technical solutions were possible. Psychological factors may cause incidents not to be documented or discussed. Incident experience seldom leads to preventive measures. Interaction between structural, social and psychological factors seemed to explain this. Questionnaires, observations and interview data suggested that some increase in safety work took place during the intervention. After the discussions the participants perceived risks to be less manageable. The study indicated that, although sensitive to dropout, participative safety interventions in fishery are feasible and may be effective. A longer or more intensive intervention may be necessary in order to progress from problem orientation to action orientation.     

电子商务推荐系统中群体用户推荐问题研究

尽管传统的电子商务推荐系统在个体用户推荐方面取得了巨大成功,但它并不适用于向群体用户进行推荐。随着虚拟社区中群体用户的不断增加,构建群体推荐系统,向群体用户提供个性化推荐,减少他们搜集信息所耗费的时间和精力显得越来越重要。基于此,本文提出了一种新颖的推荐方法—结合领域专家法的群体用户推荐算法。该算法以基于项目的协同过滤技术为基础,根据群体成员间的相互作用确定群体偏好,由群体偏好产生推荐,推荐过程中存在的成员未评分项采用领域专家法进行预测填充,此外本文算法还考虑了成员间相似关系对推荐质量的影响。实验结果表明了本文算法的有效性。