基于CCSD模型的第三方物流供应商的评价与选择

首先设计了第三方物流供应商的评价指标体系;然后运用CCSD模型确定指标的客观权重,并分析了方案排序和最优方案保持不变的权重范围;最后进行实证研究,为企业选择第三方物流供应商提供了理论依据。     

Asymptotic confidence intervals for the effect of closing an area upon the number of birds' nests

Abstract  The methodological discussion in D e R oos -S chaafsma (1981) Section 6 is continued by proposing asymptotic methods as a substitute for laborious confidence interval computations.     

Taking ‘Don’t Knows’ as Valid Responses: A Multiple Complete Random Imputation of Missing Data

Incomplete data is a common problem of survey research. Recent work on multiple imputation techniques has increased analysts’ awareness of the biasing effects of missing data and has also provided a convenient solution. Imputation methods replace non-response with estimates of the unobserved scores. In many instances, however, non-response to a stimulus does not result from measurement problems that inhibit accurate surveying of empirical reality, but from the inapplicability of the survey question. In such cases, existing imputation techniques replace valid non-response with counterfactual estimates of a situation in which the stimulus is applicable to all respondents. This paper suggests an alternative imputation procedure for incomplete data for which no true score exists: multiple complete random imputation, which overcomes the biasing effects of missing data and allows analysts to model respondents’ valid ‘I don’t know’ answers.     

多维度数据挖掘中目标相关维度选取方法对比

在数据挖掘过程中需要挑选出与目标维度相关的维度,可以通过相关行业的经验挑选,也可采用相关性方法挑选。文章通过对比2种方法在直投市场客户目标选取中的运用,得出比较结论,并对使用这2种方法同的结果作了对比。     

刑侦数据仓库模型设计与数据挖掘技术应用

针对当前刑侦海量档案数据信息,首先在分析其数据跨平台、复杂化和多样性特点的基础上,设计了刑侦数据仓库的概念模型、逻辑模型和物理模型;接着针对刑侦数据仓库及数据挖掘技术,对已有的刑侦档案数据进行信息整合和数据挖掘,获取大量的有用知识,这些知识在促进刑侦研究工作的同时,对一线的实际刑侦工作具有很大的参考价值;最后,文章给出了面向刑侦档案数据信息的仓库模型,针对其数据挖掘系统框架提出了相应的数据挖掘方法,为进一步的刑侦数据信息联机分析处理和有用信息挖掘以及为公安安全防范决策服务。     

不完全信息下物流产业融合模式选择研究

物流产业融合模式的选择是物流产业融合的实践环节之一,其正确选择可以促进物流企业的发展和物流产业的升级。在物流企业对物流产业融合模式选择的过程中,企业之间存在博弈关系。在不完全信息条件下,物流产业融合模式的选择与企业的产业延伸能力相关,取决于物流需求和双赢的利益,是一个循环往复的过程。     

Model selection in toxicity studies

In toxicity studies, model mis‐specification could lead to serious bias or faulty conclusions. As a prelude to subsequent statistical inference, model selection plays a key role in toxicological studies. It is well known that the Bayes factor and the cross‐validation method are useful tools for model selection. However, exact computation of the Bayes factor is usually difficult and sometimes impossible and this may hinder its application. In this paper, we recommend to utilize the simple Schwarz criterion to approximate the Bayes factor for the sake of computational simplicity. To illustrate the importance of model selection in toxicity studies, we consider two real data sets. The first data set comes from a study of dietary fortification with carbonyl iron in which the Bayes factor and the cross‐validation are used to determine the number of sub‐populations in a mixture normal model. The second example involves a developmental toxicity study in which the selection of dose–response functions in a beta‐binomial model is explored.     

多维度数据挖掘中目标相关维度选取方法对比

在数据挖掘过程中需要挑选出与目标维度相关的维度,可以通过相关行业的经验挑选,也可采用相关性方法挑选。文章通过对比2种方法在直投市场客户目标选取中的运用,得出比较结论,并对使用这2种方法同的结果作了对比。     

Utilising Recent Advancements in Techniques for the Analysis of Incomplete Multivariate Data to Improve the Data Quality Management of Current Academic Research

This paper discusses the importance of managing data quality in academic research in its relation to satisfying the customer. This focus is on the data completeness objectivedimension of data quality in relation to recent advancements which have been made in the development of methods for analysing incomplete multivariate data. An overview and comparison of the traditional techniques with the recent advancements are provided. Multiple imputation is also discussed as a method of analysing incomplete multivariate data, which can potentially reduce some of the biases which can occur from using some of the traditional techniques. Despite these recent advancements in the analysis of incomplete multivariate data, evidence is presented which shows that researchers are not using these techniques to manage the data quality of their current research across a variety of academic disciplines. An analysis is then provided as to why these techniques have not been adopted along with suggestions to improve the frequency of their use in the future. Source-Reference. The ideas for this paper originated from research work on David J. Fogarty's Ph.D. dissertation. The subject area is the use of advanced techniques for the imputation of incomplete multivariate data on corporate data warehouses.     

Asymptotically efficient recursive estimation for incomplete data models using the observed information

For a recursive maximum-likelihood estimator with step lengths decaying as 1/n, an adaptive matrix needs to be incorporated to obtain asymptotic efficiency. Ideally, this matrix should be chosen as the inverse Fisher information matrix, which is usually very difficult to compute for incomplete data models. In this paper we give conditions under which the observed information can be incorporated into the recursive procedure to yield an efficient estimator, and we also investigate the finite sample properties of these estimators by simulation.