Combining information for prediction in linear regression

This article deals with the prediction problem in linear regression where the measurements are obtained using k different devices or collected from k different independent sources. For the case of k=2, a Graybill-Deal type combined estimtor for the regression parameters is shown to dominate the individual least squares estimators under the covariance criterion. Two predictors ŷ _c and ŷ _p are proposed. ŷ _c is based on a combined estimator of the regression coefficient vector, and ŷ _p is obtained by combining the individual predictors from different models. Prediction mean square errors of both predictors are derived. It is shown that the predictor ŷ _p is better than the individual predictors for k≥2 and the predictor ŷ _c is better than the individual predictors for k=2. Numerical comparison between ŷ _c and ŷ _p shows that the former is superior to the latter for the case k=2.     

反倾销应诉的集体行动研究——行业协会克服"搭便车"机制的思考

本文针时我国企业在反倾销共同应诉中的不合作或者“搭使车”问题，试图探讨行业协会作为一种治理机制，如何提供或设计一种有效的制度安排（如俱乐部规范），把一群相互依存的企业组织起来，并通过自主性努力克服不合作或“搭便车”，以取得长期的共同利益的实现。     

标准化在油藏监测工作中的作用

以油藏监测工作中的油藏压力监测为例，介绍了标准化工作在压力资料录取及解释分析中的作用。并用实例说明了标准化作业在油田开发方案的制定及油藏压力监测中的应用。     

Predicting the geo-temporal variations of crime and disorder

Traditional police boundaries—precincts, patrol districts, etc.—often fail to reflect the true distribution of criminal activity and thus do little to assist in the optimal allocation of police resources. This paper introduces methods for crime incident forecasting by focusing upon geographical areas of concern that transcend traditional policing boundaries. The computerised procedure utilises a geographical crime incidence-scanning algorithm to identify clusters with relatively high levels of crime (hot spots). These clusters provide sufficient data for training artificial neural networks (ANNs) capable of modelling trends within them. The approach to ANN specification and estimation is enhanced by application of a novel and noteworthy approach, the Gamma test (GT).     

Analysing Materials and Energy Flows in an Industrial District using an Enterprise Input–Output Model

Recent approaches to sustainable development leave much room for policies at a local level. In fact, it is becoming evident that targets such as increasing resource productivity, preserving natural cycles, or extending the present level of welfare, are best pursued within the confines of a local area. In particular, environmental changes are best brought about by considering local systems of firms as cornerstones of cooperative strategies and using data on materials and energy use in physical terms. In this paper, an enterprise input–output model is developed for an industrial district, i.e. a local group of firms specialized in the production of a single final output. The model allows for a detailed quantitative analysis of materials and energy flows and the consequent generation of waste and pollution. As a planning tool, the model may be used to evaluate alternative scenarios, such as the possibility of re- using waste taking account of sustainability requirements. An empirical case study applies the model to an industrial district in Southern Italy producing leather sofas.     

The Multilevel Approach to Repeated Measures for Complete and Incomplete Data

Repeated measurements often are analyzed by multivariate analysis of variance (MANOVA). An alternative approach is provided by multilevel analysis, also called the hierarchical linear model (HLM), which makes use of random coefficient models. This paper is a tutorial which indicates that the HLM can be specified in many different ways, corresponding to different sets of assumptions about the covariance matrix of the repeated measurements. The possible assumptions range from the very restrictive compound symmetry model to the unrestricted multivariate model. Thus, the HLM can be used to steer a useful middle road between the two traditional methods for analyzing repeated measurements. Another important advantage of the multilevel approach to analyzing repeated measures is the fact that it can be easily used also if the data are incomplete. Thus it provides a way to achieve a fully multivariate analysis of repeated measures with incomplete data. This revised version was published online in June 2006 with corrections to the Cover Date.     

A comparative site evaluation study for relocating Japanese government agencies out of Tokyo

As a national project of Japan, plans for the relocation of several government agencies out of Tokyo have been ongoing. This paper is concerned with the problem of site selection for this project. The National Land Agency, the agency responsible for this project, has declared that the process of site selection should be rational, open to the public and easily understandable. In an effort to meet these requirements, we propose a consensus-making method for reaching a group decision, based on a combination of the analytic hierarchy process (AHP) and the Assurance Region model of data envelopment analysis (DEA). Several strategic uses of these methodologies, e.g., Delphi procedures, are also discussed. Based on these analyses, the “Wise Men” Committee for deciding the best site has chosen two from among ten contenders, one from the North-East and the other from the Central part of Japan, as candidates for the best relocation site. We could not discriminate between the two with respect to the multiple criteria chosen for evaluating sites. The Committee recommended the two sites to the Prime Minister at the end of December 1999. The Prime Minister reported this conclusion to the Diet. This topic is currently the focus of political discussions in the Diet. In this report, the authors summarize the decision-making processes that the Wise Men Committee followed, putting emphasis on the methodological aspects.     

Forecasting residential burglary

Following the work of Dhiri et al. [Modelling and predicting property crime trends. Home Office Research Study 198 (1999). London: HMSO] at the Home Office predicting recorded burglary and theft for England and Wales to the year 2001, econometric and time series models were constructed for predicting recorded residential burglary to the same date. A comparison between the Home Office econometric predictions and the less alarming econometric predictions made in this paper identified the differences as stemming from the particular set of variables used in the models. However, the Home Office and one of our econometric models adopted an error correction form which appeared to be the main reason why these models predicted increases in burglary. To identify the role of error correction in these models, time series models were built for the purpose of comparison, all of which predicted substantially lower numbers of residential burglaries. The years 1998–2001 appeared to offer an opportunity to test the utility of error correction models in the analysis of criminal behaviour. Subsequent to the forecasting exercise carried out in 1999, recorded outcomes have materialised, which point to the superiority of time series models compared to error correction models for the short-run forecasting of property crime. This result calls into question the concept of a long-run equilibrium relationship for crime.     

IP电话系统中SNMP代理的实现

结合对简单网络管理协议（SNMP）的研究，分析了Agent 网管代理开发包，并介绍了利用Agnet 实现IP电话系统中SNMP代理的过程。     

An investigation of transformation-based prediction interval for the Weibull median life

Statistical inference based on the Weibull distribution, a distribution widely used in reliability and survival analysis, is usually difficult as it often involves numerical computation and approximation. However, this distribution can be transformed to near-normality by a simple power transformation. Based on this transformation, a prediction interval (PI) for its median can be easily constructed through an inverse transformation. The procedure for selecting the best power transformation through minimizing Kullback-Leibler information is described. The property of this transformation-based PI is investigated. Simple correction factors are also proposed. It is shown that the transformation-based PI with corrections performs well, irrespective of the sample size and parameter values. Simulation results show that the new PI generally outperforms the existing PI. Numerical examples are given for illustration.