Generalized empirical likelihood inference in partial linear regression model for longitudinal data

In this paper, empirical likelihood inference for longitudinal data within the framework of partial linear regression models are investigated. The proposed procedures take into consideration the correlation within groups without involving direct estimation of nuisance parameters in the correlation matrix. The empirical likelihood method is used to estimate the regression coefficients and the baseline function, and to construct confidence intervals. A nonparametric version of Wilk's theorem for the limiting distribution of the empirical likelihood ratio is derived. Compared with methods based on normal approximations, the empirical likelihood does not require consistent estimators for the asymptotic variance and bias. The finite sample behaviour of the proposed method is evaluated with simulation and illustrated with an AIDS clinical trial data set.     

A Bayesian analysis of a change in the parameters of autoregressive time series

In this article, we consider a Bayesian analysis of a possible change in the parameters of autoregressive time series of known order p, AR(p). An unconditional Bayesian test based on highest posterior density (HPD) credible sets is determined. The test is useful to detect a change in any one of the parameters separately. Using the Gibbs sampler algorithm, we approximate the posterior densities of the change point and other parameters to calculate the p-values that define our test.     

中国海洋经济综合发展水平的统计测度

从基础发展、产业结构、科教发展和可持续四个方面对2001-2012年中国海洋经济的综合发展水平进行了统计测度,结果发现:地区海洋经济综合发展水平可分为三个层次,不同层次的作用因素不尽相同;广东、上海和山东三个地区的海洋经济综合发展水平在样本期间内交替领先,同时不同地区的发展速度和发展水平变化各有特点;东部区域现阶段可持续发展水平较低,而南部区域主要依靠产业结构升级拉动海洋经济。最后,提出了相应的政策建议。     

Classical testing in functional linear models

We extend four tests common in classical regression – Wald, score, likelihood ratio and F tests – to functional linear regression, for testing the null hypothesis, that there is no association between a scalar response and a functional covariate. Using functional principal component analysis, we re-express the functional linear model as a standard linear model, where the effect of the functional covariate can be approximated by a finite linear combination of the functional principal component scores. In this setting, we consider application of the four traditional tests. The proposed testing procedures are investigated theoretically for densely observed functional covariates when the number of principal components diverges. Using the theoretical distribution of the tests under the alternative hypothesis, we develop a procedure for sample size calculation in the context of functional linear regression. The four tests are further compared numerically for both densely and sparsely observed noisy functional data in simulation experiments and using two real data applications.     

海洋钻井隔水管顶张力分析

隔水管系统足海洋钻井的主要设备之-本丈对描述隔水管运动的四阶非线性偏徽分方程和边界条件进行了简化，并应用有限差分法对隔水管的受力情况进行数值分析.作用在隔水管上的顶张力，使用四种海况进行研究，并编制了相应的计算程序.通过实例计葬，与美国应力工程服务奋司的计葬结果进行比较，结果完全-致.末文提供的动态分析方法及计葬程序具有方法简便、精确度高和机时省千优点，对设计和校核隔水管系统具有实用价值.     

Asymptotic distribution of regression correlation coefficient for Poisson regression model

This study treats an asymptotic distribution for measures of predictive power for generalized linear models (GLMs). We focus on the regression correlation coefficient (RCC) that is one of the measures of predictive power. The RCC, proposed by Zheng and Agresti is a population value and a generalization of the population value for the coefficient of determination. Therefore, the RCC is easy to interpret and familiar. Recently, Takahashi and Kurosawa provided an explicit form of the RCC and proposed a new RCC estimator for a Poisson regression model. They also showed the validity of the new estimator compared with other estimators. This study discusses the new statistical properties of the RCC for the Poisson regression model. Furthermore, we show an asymptotic normality of the RCC estimator.     

Regularized proportional odds models

The proportional odds model (POM) is commonly used in regression analysis to predict the outcome for an ordinal response variable. The maximum likelihood estimation (MLE) approach is typically used to obtain the parameter estimates. The likelihood estimates do not exist when the number of parameters, p, is greater than the number of observations n. The MLE also does not exist if there are no overlapping observations in the data. In a situation where the number of parameters is less than the sample size but p is approaching to n, the likelihood estimates may not exist, and if they exist they may have quite large standard errors. An estimation method is proposed to address the last two issues, i.e. complete separation and the case when p approaches n, but not the case when p>n. The proposed method does not use any penalty term but uses pseudo-observations to regularize the observed responses by downgrading their effect so that they become close to the underlying probabilities. The estimates can be computed easily with all commonly used statistical packages supporting the fitting of POMs with weights. Estimates are compared with MLE in a simulation study and an application to the real data.     

Confidence intervals for treatment effect from restricted maximum likelihood

An explicit form of confidence intervals for the treatment effect in random effects meta-analysis model obtained from Harville–Jeske–Kenward–Roger approach is given. These restricted likelihood based intervals are compared to alternative procedures commonly used in collaborative studies when the number of participants is small and study-specific variances are heterogeneous. Monte Carlo simulation experiments show that the former intervals have quite conservative coverage probabilities and favor the latter intervals.     

Selected indicators for evaluation of eco-innovation projects

This paper addresses a particular innovation projects category, that is, eco-innovation, in line with the fact that a lot of research has been recently carried out in this area, due to increasing attention. Eco-innovation projects are related to development and introducing the new products, processes and services that reduce the overall negative impact on the environment, putting the business and innovation together to create sustainable solutions. The aim of our research is developing a model for prioritization of the most frequent eco-innovation projects in the recycling field. It should be one of the key challenges for national economies as it happens to be one of the current priorities and burning issues for the European Commission (Horizon 2020, CIP, COSME, etc.), giving the proposed model framework. The ranking has been established with regard to common national economy benefits defined through identified criteria, by applying the well-known analytic hierarchy process method of multi-criteria decision-making.     

基于DEA模型的动漫产业竞争力分析

本文概述了产业竞争力的基本内涵以及波特的钻石模型,基于SWOT对杭州动漫产业竞争力进行了详细的定性分析,并运用DEA模型对动漫产业竞争力进行了相应的评价,从多个角度为杭州动漫产业竞争力的提升提出了对策建议。