Bayesian estimation and influence diagnostics of generalized partially linear mixed-effects models for longitudinal data

This paper develops a Bayesian approach to obtain the joint estimates of unknown parameters, nonparametric functions and random effects in generalized partially linear mixed models (GPLMMs), and presents three case deletion influence measures to identify influential observations based on the φ-divergence, Cook's posterior mean distance and Cook's posterior mode distance of parameters. Fisher's iterative scoring algorithm is developed to evaluate the posterior modes of parameters in GPLMMs. The first-order approximation to Cook's posterior mode distance is presented. The computationally feasible formulae for the φ-divergence diagnostic and Cook's posterior mean distance are given. Several simulation studies and an example are presented to illustrate our proposed methodologies.     

Diagnostics in elliptical regression models with stochastic restrictions applied to econometrics

We propose an influence diagnostic methodology for linear regression models with stochastic restrictions and errors following elliptically contoured distributions. We study how a perturbation may impact on the mixed estimation procedure of parameters in the model. Normal curvatures and slopes for assessing influence under usual schemes are derived, including perturbations of case-weight, response variable, and explanatory variable. Simulations are conducted to evaluate the performance of the proposed methodology. An example with real-world economy data is presented as an illustration.     

Goodness-of-fit tests of generalized linear mixed models for repeated ordinal responses

Categorical longitudinal data are frequently applied in a variety of fields, and are commonly fitted by generalized linear mixed models (GLMMs) and generalized estimating equations models. The cumulative logit is one of the useful link functions to deal with the problem involving repeated ordinal responses. To check the adequacy of the GLMMs with cumulative logit link function, two goodness-of-fit tests constructed by the unweighted sum of squared model residuals using numerical integration and bootstrap resampling technique are proposed. The empirical type I error rates and powers of the proposed tests are examined by simulation studies. The ordinal longitudinal studies are utilized to illustrate the application of the two proposed tests.     

基于熵权法的财务危机预警指标选择研究

合理选择财务指标是财务危机预警研究中的重要内容,用定性和定量相结合的方法进行选择是科学有效的方法之一。首先根据初选原则对财务指标进行了初步选择,然后提出了一种基于熵权的变量选择方法,对初选的财务指标进行定量筛选,进行实证研究,最终确定了应用于财务危机预警的财务指标。     

基于模糊信息熵的环境管理过程控制能力

建立了环境管理过程的闭环过程控制系统模型,用模糊控制系统表达环境管理过程中的不确定性,分析了环境管理过程的控制能力,建立了过程控制能力函数。将信息熵引入模糊环境过程控制函数的参数识别,建立了环境管理过程度量方法。同时,建立了环境管理过程决策支持系统,以实现环境管理过程的参数识别和能力评估。     

影响企业扩张度的因素及其熵描述

通过对影响企业扩张度因素的分析以及熵思想下企业扩张度的重新界定,指出企业扩张必须遵循的原则就是,扩张必须基于自身核心竞争能力等内部基本元素的强弱与外部环境来确定其扩张的规模、速度以及经济范围,以便达到内部熵能最小和持续发展的目的。     

熵理论与和谐社会

熵理论揭示了任何事物和过程都是有序和无序的辩证统一,人类社会的发展也是有序和无序的辩证统一。构建和谐社会的实质就是克服无序(不和谐)、创建有序(和谐),实现安定有序。这是建设中国特色社会主义的重大任务。     

欧盟知识产权跨界禁令与竞争规则研究

《布鲁塞尔规则》2001年取代了《布鲁塞尔公约》,成为新的欧盟指令来规范相关的跨界国际私法的争议。欧盟委员会2004年针对适用《欧洲经济共同体条约》第81条的规定颁布了配套的指令TTBER和适用这个指令的指南。指令第3条规定了两个协议合作的企业市场份额门槛的认定标准,这个标准取代了过去的分类方法,在欧洲引起广泛的讨论。欧盟知识产权跨界禁令和竞争规则研究,突破了知识产权的地域性原则,完善了知识产权的竞争规则,并提供了相应的法律救济,有助于中国政府指导自己的企业与欧盟开展贸易。     

Practical Maximum Pseudolikelihood for Spatial Point Patterns(with Discussion)

This paper describes a technique for computing approximate maximum pseudolikelihood estimates of the parameters of a spatial point process. The method is an extension of Berman & Turner's (1992) device for maximizing the likelihoods of inhomogeneous spatial Poisson processes. For a very wide class of spatial point process models the likelihood is intractable, while the pseudolikelihood is known explicitly, except for the computation of an integral over the sampling region. Approximation of this integral by a finite sum in a special way yields an approximate pseudolikelihood which is formally equivalent to the (weighted) likelihood of a loglinear model with Poisson responses. This can be maximized using standard statistical software for generalized linear or additive models, provided the conditional intensity of the process takes an 'exponential family' form. Using this approach a wide variety of spatial point process models of Gibbs type can be fitted rapidly, incorporating spatial trends, interaction between points, dependence on spatial covariates, and mark information.     

A comparative study of confidence intervals to assess biosimilarity from analytical data

Assessment of analytical similarity of tier 1 quality attributes is based on a set of hypotheses that tests the mean difference of reference and test products against a margin adjusted for standard deviation of the reference product. Thus, proper assessment of the biosimilarity hypothesis requires statistical tests that account for the uncertainty associated with the estimations of the mean differences and the standard deviation of the reference product. Recently, a linear reformulation of the biosimilarity hypothesis has been proposed, which facilitates development and implementation of statistical tests. These statistical tests account for the uncertainty in the estimation process of all the unknown parameters. In this paper, we survey methods for constructing confidence intervals for testing the linearized reformulation of the biosimilarity hypothesis and also compare the performance of the methods. We discuss test procedures using confidence intervals to make possible comparison among recently developed methods as well as other previously developed methods that have not been applied for demonstrating analytical similarity. A computer simulation study was conducted to compare the performance of the methods based on the ability to maintain the test size and power, as well as computational complexity. We demonstrate the methods using two example applications. At the end, we make recommendations concerning the use of the methods.