Excel规划求解工具在多目标规划中的应用

多目标决策方法是从20世纪70年代中期发展起来的一种决策分析方法。该方法已广泛应用于人口、环境、教育、能源、交通、经济管理等多个领域。文章采用多目标决策方法中分层序列法的思想,应用Excel的规划求解工具,对多目标规划问题进行应用研究,并以实例加以说明。     

结构变化的节能潜力计算的方法论研究

本文介绍了几种计算节能的因素贡献份额的方法,通过分析指出了部门能源强度与技术贡献之间的差别。在定义明确后,经公式计算与分解处理的结果表明,中国在1995～2002年总能源强度下降中,结构节能贡献大约在60%以上; 2002年以后总能源强度呈上升趋势,这时的结构变化推动能源强度上升(对节能起负作用)。符合客观实际的结论应是,结构变化对能源强度变化的影响还是很大的。     

A Bayesian Approach to Parameter Estimation in the Presence of Spatial Missing Data

The missing data problem has been widely addressed in the literature. The traditional methods for handling missing data may be not suited to spatial data, which can exhibit distinctive structures of dependence and/or heterogeneity. As a possible solution to the spatial missing data problem, this paper proposes an approach that combines the Bayesian Interpolation method [Benedetti, R. & Palma, D. (1994) Markov random field-based image subsampling method, Journal of Applied Statistics, 21(5), 495–509] with a multiple imputation procedure. The method is developed in a univariate and a multivariate framework, and its performance is evaluated through an empirical illustration based on data related to labour productivity in European regions.     

A goal programming-discriminant function approach to the estimation of an empirical production function based on DEA results

This paper describes a new frontier estimation procedure which relies on results obtained from Data Envelopment Analysis (DEA). The paper reviews some of the earlier works in this area and points out potential difficulties with them. It further suggests ways to validate such developments. A procedure is constructed on the basis of a Goal Programming (GP)-Discriminant Function model developed in stages during the 1980s. A numerical example is used to illustrate the proposed procedure. Then, an extensive simulation in which the GP-based procedure is compared to three regression-based techniques is presented. The simulation results clearly indicate the superiority of the proposed technique over the regression alternatives.     

A general framework for approximate sampling with an application to generating points on the boundary of bounded convex regions

We consider the problem of generating a sample of points according to some given probability distribution over some region. We give a general framework for constructing approximate sampling algorithms based on the theory of Markov chains. In particular, we show how it can be proven that a Markov chain has a limiting distribution. We apply these results to prove convergence for a class of so-called Shake-and-Bake algorithms, which can be used to approximate any absolutely continuous distribution over the boundary of a full-dimensional convex body.     

A multiobjective interval programming model to explore the trade-offs among different aspects of job satisfaction under different scenarios

The need for greater concern about job quality/satisfaction seems clear, due to its potential link with workers' productivity, to the extent it affects employees' quitting behaviour, absenteeism, turnover, and firms' productivity. In order to guide managers and policy makers when making decisions related to future hiring of human resources, a multiobjective interval programming model, based on the results of an econometric estimation, is suggested where different (and conflicting) aspects of job satisfaction are considered. The modelling framework thus obtained allows assessing the trade-offs among the different aspects of job satisfaction under different scenarios herein given within interval ranges. Data obtained from Spain are used to carry out the model's instantiation. Possibly efficient solutions are then generated with the help of scalarizing problems relying on reference point-based methods. The solution approach herein proposed allows computing with a lower computational effort the closest “possibly” efficient solutions attainable regarding their corresponding interval ideal solutions. Overall, the results obtained highlight the trade-off between earnings and quality of life, particularly under a pessimistic scenario, with the maximization of earnings leading to the lowest value of the working times. Conversely, the lowest value obtainable for earnings is reached with the consideration of both scenarios when the maximization of the satisfaction of the quality of life seekers is attained. Finally, the trade-off between less prone to risk workers and quality of life seekers is also revealed, with the lowest job security levels found in the solution that maximizes working times.     

A stochastic differential equation approach to the analysis of the 2017 and 2019 UK general election polls

Human dynamics and sociophysics build on statistical models that can shed light on and add to our understanding of social phenomena. We propose a generative model based on a stochastic differential equation that enables us to model the opinion polls leading up to the 2017 and 2019 UK general elections and to make predictions relating to the actual results of the elections. After a brief analysis of the time series of the poll results, we provide empirical evidence that the gamma distribution, which is often used in financial modelling, fits the marginal distribution of this time series. We demonstrate that the proposed poll-based forecasting model may improve upon predictions based solely on polls. The method uses the Euler–Maruyama method to simulate the time series, measuring the prediction error with the mean absolute error and the root mean square error, and as such could be used as part of a toolkit for forecasting elections.     

A new method to assess the degree of information rigidity using fixed-event forecasts

We propose a new method to explore the information content of fixed-event forecasts and estimate structural parameters that are keys to sticky and noisy information models. Estimation follows a regression-based framework in which estimated coefficients map one-to-one with parameters that measure the degree of information rigidity. The statistical characterization of regression errors explores the laws that govern expectation formation under sticky and noisy information, that is, they are coherent with the theory. This strategy is still unexplored in the literature and potentially enhances the reliability of inference results. The method also allows linking estimation results to the signal-to-noise ratio, an important parameter of noisy information models. This task cannot be accomplished if one adopts an “agnostic” characterization of regression errors. With regard to empirical results, they show a substantial degree of information rigidity in the countries studied. They also suggest that the theoretical characterization of regression errors yields a more conservative picture of the uncertainty surrounding parameter estimates.