A global risk assessment model for civil wars

In this study, we construct a multivariate model that assesses the risk of an outbreak of civil war in a country over a period of 5 years into the future. In addition to structural factors of state weakness, which have dominated the literature on civil war onset, this model includes repression of basic human rights to personal integrity – an important harbinger of wars to come – as an aspect of state behavior. Our aim is not to explore the causal factors of civil war onset, but to build a model that includes indicators that correlate with civil war outbreak and may be used to predict it. Based on two versions of the model – logit and neural network – out-of-sample risk assessments for three different time periods are generated and compared to the historical record of civil war outbreak during those years. In addition, the model’s ability to produce in-sample risk assessments over a 5-year period is tested. Finally, we compute truly predictive civil war risk assessments for all countries for which data are available, for the years 2008–2012. The analyses show that with a relatively simple model and based on publicly available data sources, meaningful civil war risk assessments can be computed. The quality of the predictions exceeds that of prominent studies, in which the risk of interstate war is assessed.     

C472. The expected value of a conditional variance: An upper bound

The article derives Bartlett corrections for improving the chi-square approximation to the likelihood ratio statistics in a class of symmetric nonlinear regression models. This is a wide class of models which encompasses the t model and several other symmetric distributions with longer-than normal tails. In this paper we present, in matrix notation, Bartlett corrections to likelihood ratio statistics in nonlinear regression models with errors that follow a symmetric distribution. We generalize the results obtained by Ferrari, S. L. P. and Arellano-Valle, R. B. (1996). Modified likelihood ratio and score tests in linear regression models using the t distribution. Braz. J. Prob. Statist., 10, 15–33, who considered a t distribution for the errors, and by Ferrari, S. L. P. and Uribe-Opazo, M. A. (2001). Corrected likelihood ratio tests in a class of symmetric linear regression models. Braz. J. Prob. Statist., 15, 49–67, who considered a symmetric linear regression model. The formulae derived are simple enough to be used analytically to obtain several Bartlett corrections in a variety of important models. We also present simulation results comparing the sizes and powers of the usual likelihood ratio tests and their Bartlett corrected versions.     

The concept of risk unbiasedness in statistical prediction

This paper extends the concept of risk unbiasedness for applying to statistical prediction and nonstandard inference problems, by formalizing the idea that a risk unbiased predictor should be at least as close to the “true” predictant as to any “wrong” predictant, on the average. A novel aspect of our approach is measuring closeness between a predicted value and the predictant by a regret function, derived suitably from the given loss function. The general concept is more relevant than mean unbiasedness, especially for asymmetric loss functions. For squared error loss, we present a method for deriving best (minimum risk) risk unbiased predictors when the regression function is linear in a function of the parameters. We derive a Rao–Blackwell type result for a class of loss functions that includes squared error and LINEX losses as special cases. For location-scale families, we prove that if a unique best risk unbiased predictor exists, then it is equivariant. The concepts and results are illustrated with several examples. One interesting finding is that in some problems a best unbiased predictor does not exist, but a best risk unbiased predictor can be obtained. Thus, risk unbiasedness can be a useful tool for selecting a predictor.     

A Note on Trend Decomposition: The 'Classical' Approach Revisited with an Application to Surface Temperature Trends

This note reconsiders the 'classical' approach to trend estimation and presents a modern treatment of this technique that enables trend filters which incorporate end-effects to be constructed easily and efficiently. The approach is illustrated by estimating recent Northern Hemispheric temperature trends. In so doing, it shows how classical trend models may be selected in empirical applications and indicates how this choice determines the properties of the latest trend estimates.     

Prediction and Decision Making in Child Welfare

Use of computer-based statistical models to support decision making may reduce the subjectivity and uncertainty in this process of more acceptable levels. Multivariate procedures capture the collective experience of many workers by integrating information from a large number of variables and cases. Once a predictive model has been developed and validated, the profile of new cases can be compared to cases already on the data base. The probability of critical case events such as admission tocare, occurence of abuse, and placement breakdown can be identified. Such capabilities could also be incorporated into the development of computerized information systems. This would faciliate case planning and a more equitable and effective match between client needs and resource utilization.     

Minimum,maximum, and average spherical prediction variances for central composite and box-behnken designs

Single value design optimality criteria are often considered when selecting a response surface design. An alternative to a single value criterion is to evaluate prediction variance properties throughout the experimental region and to graphically display the results in a variance dispersion graph (VDG) (Giovannitti-Jensen and Myers (1989)). Three properties of interest are the spherical average, maximum, and minimum prediction variances. Currently, a computer-intensive optimization algorithm is utilized to evaluate these prediction variance properties. It will be shown that the average, maximum, and minimum spherical prediction variances for central composite designs and Box-Behnken designs can be derived analytically. These three prediction variances can be expressed as functions of the radius and the design parameters. These functions provide exact spherical prediction variance values eliminating the implementation of extensive computing involving algorithms which do not guarantee convergence. This research is concerned with the theoretical development of these analytical forms. Results are presented for hyperspherical and hypercuboidal regions.     

Prediction intervals for ordinary and dual generalized order statistics from two independent sequences

ABSTRACT

Based on the observed dual generalized order statistics drawn from an arbitrary unknown distribution, nonparametric two-sided prediction intervals as well as prediction upper and lower bounds for an ordinary and a dual generalized order statistic from another iid sequence with the same distribution are developed. The prediction intervals for dual generalized order statistics based on the observed ordinary generalized order statistics are also developed. The coverage probabilities of these prediction intervals are exact and free of the parent distribution, F. Finally, numerical computations and real examples of the coverage probabilities are presented for choosing the appropriate limits of the prediction.     

我国高校绩效管理研究现状、问题与发展趋势

绩效管理已日渐成为高校人力资源管理的研究热点,本文通过我国高校绩效管理目前研究的三个层面、两个方向和几个关键问题等,对目前研究现状进行阐述,发现存在的一些问题,并预见我国高校绩效管理研究未来的发展趋势。     

近年来我国学前融合教育研究综述

当前，学前融合教育已经成为国际学前特殊教育的发展趋势。文章探讨了近年来我国学前融合教育的研究内容，分析了我国学前融合教育目前存在的现状，以期对今后开展学前融合教育有所启发。     

Econometric Analysis of Costing System Components in Rail Rate Regulation

In response to Congressional directive, the Interstate Commerce Commission (ICC) has created a railroad costing system that includes as key components ratios designed to estimate variable costs associated with freight transportation. The estimated variability ratios are used to determine freight surcharges, jurisdictional threshold rates, and basic rail rates in administrative law and federal court proceedings. In this article we assess the quality and reliability of the estimated variability ratios and their components against standards from economic theory and statistical theory and practice. Our work includes reproduction of the naive ICC regressions, updated naive regressions for the latest data set, estimation based on more secure econometric foundations, and sensitivity analyses comparing alternative estimation procedures. Fundamental questions arise concerning the scientific and evidentiary standards that are required for econometric methodology in policy making and regulatory activities.