Variable precision rough set theory and data discretisation: an application to corporate failure prediction

Since the seminal work of Pawlak (International Journal of Information and Computer Science, 11 (1982) 341–356) rough set theory (RST) has evolved into a rule-based decision-making technique. To date, however, relatively little empirical research has been conducted on the efficacy of the rough set approach in the context of business and finance applications. This paper extends previous research by employing a development of RST, namely the variable precision rough sets (VPRS) model, in an experiment to predict between failed and non-failed UK companies. It also utilizes the FUSINTER discretisation method which neglates the influence of an ‘expert’ opinion. The results of the VPRS analysis are compared to those generated by the classical logit and multivariate discriminant analysis, together with more closely related non-parametric decision tree methods. It is concluded that VPRS is a promising addition to existing methods in that it is a practical tool, which generates explicit probabilistic rules from a given information system, with the rules offering the decision maker informative insights into classification problems.     

烟草商业企业数字仓库的基本流程与系统功能设计研究

数字仓库的应用将协助企业保证供应、压缩库存、提高货物周转效率、节约成本。本文介绍了数字仓库的概念,研究了烟草商业企业数字仓库的基本流程与系统功能结构与设计,并对数字仓库的效益进行了评价。对这些问题的深入研究将有利于提高烟草商业企业的物流水平和服务效率。     

Data relaying with constraints in hierarchical sensor networks

In this paper we present and discuss several optimisation problems that arise in the management of data flow in wireless sensor networks (WSNets). We consider a hierarchical architecture for WSNets composed of sensors, relays, and relay gateways. Sensors send data they generate at a known average bit rate to relays in one hop. The relay nodes use a multi-hop mechanism to reach a set of assigned gateways which then forward the data directly to the base station. We are concerned with finding an assignment of relay gateways to relays so that certain constraints are satisfied. We define a unified model in which constraints such as lifetime, data delay, and data flow splitting are formulated in terms of four optimisation problem in graphs.     

Competitive and Cooperative Positioning in Supply Chain Logistics Relationships*

Cooperative logistics relationships require the sharing of information, which must be enabled by the integration of disparate information systems across partners. In this article, we theorize business‐to‐business logistics relationships should be managed using cooperative and competitive postures. Based on data from 91 dyadic relationships using interorganizational information technology (IT), we find that performance gains accrue when parties share strategic information and customize IT; mutual trust enables IT customization and strategic‐information flows and equitable relationship‐specific investments positively impact IT customization, mutual trust, and performance. Among other scholarly and practical implications discussed, partners should compete on resources for IT customization and cooperate to share strategic information. Managers tend to think of relationships with firms as polar opposites and view them as entirely cooperative or entirely competitive. Our results support active balancing and understanding of both competitive and cooperative stances. Such an approach enables conditions for participation symmetry that yields greater performance gains.     

The Job Demands-Job Control Model and absence behaviour: results of a 3-year longitudinal study

Empirical results of earlier studies only marginally supported the relevance of Karasek's Job Demands-Job Control Model for absence behaviour. Since longitudinal studies with respect to these relations were largely lacking, a four-wave panel study was carried out using data from 1755 male employees of a technical maintenance firm in the public sector. Job demands, job control, physical working conditions, and the employee's age, education, and health were measured in one year and absenteeism in the same year and in the next 3 years. Data were analysed with linear regression and Poisson regression techniques. The Poisson regression technique was superior to the linear regression technique in explaining absence. Age, health and prior absence were the best predictors of later absence behaviour. With respect to the Job Demands-Job Control Model, the main findings of the study were (1) that job control was significantly associated with a low number of simultaneous and later absence days, and (2) that, contrary to expectations, job demands were also related to a low number of simultaneous and later absence days. These results hold when age, health, education, prior absence, and working conditions are controlled for. Job control and job demands did not predict later absence frequency. In the discussion it is suggested that a high level of job demands may not only be harmful for the well-being of employees but also work as 'a pressure to attend'.     

Coverage probabilities of nonparametric simultaneous confidence bands for a survival function

Simultaneous confidence bands provide a useful adjunct to the popular Kaplan–Meier product limit estimator for a survival function, particularly when results are displayed graphically. They allow an assessment of the magnitude of sampling errors and provide a graphical view of a formal goodness-of-fit test. In this paper we evaluate a modified version of Nair's (1981) simultaneous confidence bands. The modification is based on a logistic transformation of the Kaplan–Meier estimator. We show that the modified bands have some important practical advantages.     

Bayesian Estimation and Model Choice in Item Response Models

Item response models are essential tools for analyzing results from many educational and psychological tests. Such models are used to quantify the probability of correct response as a function of unobserved examinee ability and other parameters explaining the difficulty and the discriminatory power of the questions in the test. Some of these models also incorporate a threshold parameter for the probability of the correct response to account for the effect of guessing the correct answer in multiple choice type tests. In this article we consider fitting of such models using the Gibbs sampler. A data augmentation method to analyze a normal-ogive model incorporating a threshold guessing parameter is introduced and compared with a Metropolis-Hastings sampling method. The proposed method is an order of magnitude more efficient than the existing method. Another objective of this paper is to develop Bayesian model choice techniques for model discrimination. A predictive approach based on a variant of the Bayes factor is used and compared with another decision theoretic method which minimizes an expected loss function on the predictive space. A classical model choice technique based on a modified likelihood ratio test statistic is shown as one component of the second criterion. As a consequence the Bayesian methods proposed in this paper are contrasted with the classical approach based on the likelihood ratio test. Several examples are given to illustrate the methods.     

Improved approaches to interval estimation of effective doses for a logistic dose-response curve

Asymptotic confidence (delta) intervals and intervals based upon the use of Fieller's theorem are alternative methods for constructing intervals for the <$>gamma<$>% effective doses (ED<$>_gamma<$>). Sitter and Wu (1993) provided a comparison of the two approaches for the ED<$>_{50}<$>, for the case in which a logistic dose response curve is assumed. They showed that the Fieller intervals are generally superior. In this paper, we introduce two new families of intervals, both of which include the delta and Fieller intervals as special cases. In addition we consider interval estimation of the ED<$>_{90}<$> as well as the ED<$>_{50}<$>. We provide a comparison of the various methods for the problem of constructing a confidence interval for the ED<$>_gamma<$>.     

Bayesian updating of atmospheric dispersion after a nuclear accident

Summary.  We consider a Bayesian forecasting system to predict the dispersal of contamination on a large scale grid in the event of an accidental release of radioactivity. The statistical model is built on a physical model for atmospheric dispersion and transport called MATCH. Our spatiotemporal model is a dynamic linear model where the state parameters are the (essentially, deterministic) predictions of MATCH; the distributions of these are updated sequentially in the light of monitoring data. One of the distinguishing features of the model is that the number of these parameters is very large (typically several hundreds of thousands) and we discuss practical issues arising in its implementation as a realtime model. Our procedures have been checked against a variational approach which is used widely in the atmospheric sciences. The results of the model are applied to test data from a tracer experiment.     

Marginal methods for clustered longitudinal binary data with incomplete covariates

Many analyses for incomplete longitudinal data are directed to examining the impact of covariates on the marginal mean responses. We consider the setting in which longitudinal responses are collected from individuals nested within clusters. We discuss methods for assessing covariate effects on the mean and association parameters when covariates are incompletely observed. Weighted first and second order estimating equations are constructed to obtain consistent estimates of mean and association parameters when covariates are missing at random. Empirical studies demonstrate that estimators from the proposed method have negligible finite sample biases in moderate samples. An application to the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDS) demonstrates the utility of the proposed method.