灰色模型参数辨识的新方法

给出了数列累加和的计算公式的定义 ,由累加和的运算得到了灰色模型参数的辩识方法 .通过实例计算 ,该方法具有较高的预测精度 ,为实际应用提供了切实可行的方法     

如何在概率统计课程教学中介绍Matlab

本文针对目前概率统计课程教学改革发展的需要 ,介绍了在工科概率统计课程的教学过程中如何在比较短的时间内让学生对 Matlab有个初步的认识 ,并能够使用 Matlab的统计工具箱解决一些简单的概率统计问题     

Varying Dispersion Diagnostics for Inverse Gaussian Regression Models

Homogeneity of dispersion parameters is a standard assumption in inverse Gaussian regression analysis. However, this assumption is not necessarily appropriate. This paper is devoted to the test for varying dispersion in general inverse Gaussian linear regression models. Based on the modified profile likelihood (Cox & Reid, 1987), the adjusted score test for varying dispersion is developed and illustrated with Consumer- Product Sales data (Whitmore, 1986) and Gas vapour data (Weisberg, 1985). The effectiveness of orthogonality transformation and the properties of a score statistic and its adjustment are investigated through Monte Carlo simulations.     

Modelling forces of infection by using monotone local polynomials

Summary. On the basis of serological data from prevalence studies of rubella, mumps and hepatitis A, the paper describes a flexible local maximum likelihood method for the estimation of the rate at which susceptible individuals acquire infection at different ages. In contrast with parametric models that have been used before in the literature, the local polynomial likelihood method allows this age-dependent force of infection to be modelled without making any assumptions about the parametric structure. Moreover, this method allows for simultaneous nonparametric estimation of age-specific incidence and prevalence. Unconstrained models may lead to negative estimates for the force of infection at certain ages. To overcome this problem and to guarantee maximal flexibility, the local smoother can be constrained to be monotone. It turns out that different parametric and nonparametric estimates of the force of infection can exhibit considerably different qualitative features like location and the number of maxima, emphasizing the importance of a well-chosen flexible statistical model.     

再论企业技术和市场的协同创新--基于协同学序参量概念的创新管理理论研究

创新是现代企业的唯一出路,但创新过程和结果的高度复杂性与风险性使许多企业对创新望而却步.由协同理论所揭示的序参量概念为企业技术和市场之间的联合创新提供了新的突破口.从企业生命系统的特征出发,技术和市场的联合创新就是要通过有序协同架构的构建促使创新序参量的快速形成,从而解决创新的降低风险需求问题,获取价值增加/价值创造、有效价值传递以及短期竞争优势和长期能力发展的创新整合优势.     

串联型有源电力滤波器的主要参数设计和仿真研究

串联型有源电力滤波器可较好地补偿电压源型谐波源．串联型有源电力滤波器的电路参数地补偿特性有较大的影响．本文就串联型有源电力滤波器检测电路中的低通滤波器．主电路中的高频IC滤波器等电路参数进行了．分析和选取．并利用Matlab中的电力系统仿真工具箱对串联型有源电力滤波器进行了系统建模和仿真研究．仿真结果表明．在合理的参数配置下．串联型有源电力滤波器可对电力系统中的谐波进行较好的补偿．     

Parameter Orthogonality and Bias Adjustment for Estimating Functions

Abstract.  We consider an extended notion of parameter orthogonality for estimating functions, called nuisance parameter insensitivity, which allows a unified treatment of nuisance parameters for a wide range of methods, including Liang and Zeger's generalized estimating equations. Nuisance parameter insensitivity has several important properties in common with conventional parameter orthogonality, such as the nuisance parameter causing no loss of efficiency for estimating the interest parameter, and a simplified estimation algorithm. We also consider bias adjustment for profile estimating functions, and apply the results to restricted maximum likelihood estimation of dispersion parameters in generalized estimating equations.     

A numerical approach to the Behrens–Fisher problem

The test of the hypothesis of equal means of two normal populations without assumption on the variances is usually referred to as the Behrens–Fisher Problem. Exact similar tests are known not to exist. However, excellent approximately similar “solutions” are readily available. Of these available tests and corresponding critical regions, those due to Welch, Aspin, and Trickett in the 1940s and 1950s come closest to achieving similarity. This article examines numerically the Welch–Aspin asymptotic series and the related Trickett–Welch integral equation formulations of this problem. Through examples, we illustrate that well-behaved tests can deviate from similarity by an almost incredibly small amount. Despite this, with much more extensive computation than was feasible a half-century ago, we can see irregularities which could be an empirical reflection of the known nonexistance of exact solutions.     

Tuning Parameter Selection in Penalized Frailty Models

The penalized likelihood approach of Fan and Li (2001 Fan, J., Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Association 96:1348–1360.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar], 2002 Fan, J., Li, R. (2002). Variable selection for Cox’s proportional hazards model and frailty model. The Annals of Statistics 30:74–99.[Crossref], [Web of Science ®] , [Google Scholar]) differs from the traditional variable selection procedures in that it deletes the non-significant variables by estimating their coefficients as zero. Nevertheless, the desirable performance of this shrinkage methodology relies heavily on an appropriate selection of the tuning parameter which is involved in the penalty functions. In this work, new estimates of the norm of the error are firstly proposed through the use of Kantorovich inequalities and, subsequently, applied to the frailty models framework. These estimates are used in order to derive a tuning parameter selection procedure for penalized frailty models and clustered data. In contrast with the standard methods, the proposed approach does not depend on resampling and therefore results in a considerable gain in computational time. Moreover, it produces improved results. Simulation studies are presented to support theoretical findings and two real medical data sets are analyzed.     

A Gaussian alternative to using improper confidence intervals

The problem posed by exact confidence intervals (CIs) which can be either all-inclusive or empty for a nonnegligible set of sample points is known to have no solution within CI theory. Confidence belts causing improper CIs can be modified by using margins of error from the renewed theory of errors initiated by J. W. Tukey—briefly described in the article—for which an extended Fraser's frequency interpretation is given. This approach is consistent with Kolmogorov's axiomatization of probability, in which a probability and an error measure obey the same axioms, although the connotation of the two words is different. An algorithm capable of producing a margin of error for any parameter derived from the five parameters of the bivariate normal distribution is provided. Margins of error correcting Fieller's CIs for a ratio of means are obtained, as are margins of error replacing Jolicoeur's CIs for the slope of the major axis. Margins of error using Dempster's conditioning that can correct optimal, but improper, CIs for the noncentrality parameter of a noncentral chi-square distribution are also given.