我国几种主要农产品的未来需求

收入水平是影响农产品消费需求的主要因素,需求收入弹性的大小表征着需求随收入增加的变化状况.建立可变需求弹性估计模型,使用多个年份的截面统计数据,可对我国粮食、猪肉、牛羊肉、家禽、鲜蛋、鱼虾和鲜菜的需求收入弹性进行测算,给出这7种主要农产品的需求收入弹性随着收入变化的定量估计结果,同时,可据此对我国主要农产品未来需求空间大小进行判断.     

实时动态模拟环境中多任务间的数据共享

根据不同应用程序之间有共享数据的要求,针对Windows NT系统和VC++的特点,提出通过动态链接库提供共享数据的方法,解决了Windows NT系统下实时动态模拟环境中多任务间数据共享,结果使各应用程序共同使用一个动态链接库,而需要共享的数据在该库中定义,达到了共享的目的。且也使不同语言的应用程序之间达到共享数据。通过地址访问方式还可以使各个应用程序通过间接访问方式使用共享数据。该方法简便、实用性强。     

数据包络分析中变量分类研究评述

数据包络分析的研究历史与研究者对于变量特性的认知历史是紧密联系在一起的。在数据包络分析的早期研究中,所有的投入或者产出变量都被视为可数量化、可自由处置、单角色的等。研究者逐渐认识到有些变量是不可数量化的、非自由处置的、双角色的,因此,不同目的的DEA模型被提出并加以研究。本文对数据包络分析中有关变量分类的研究进行系统性评述。     

Nonparametric Instrumental Variables Estimation of a Quantile Regression Model

We consider nonparametric estimation of a regression function that is identified by requiring a specified quantile of the regression “error” conditional on an instrumental variable to be zero. The resulting estimating equation is a nonlinear integral equation of the first kind, which generates an ill‐posed inverse problem. The integral operator and distribution of the instrumental variable are unknown and must be estimated nonparametrically. We show that the estimator is mean‐square consistent, derive its rate of convergence in probability, and give conditions under which this rate is optimal in a minimax sense. The results of Monte Carlo experiments show that the estimator behaves well in finite samples.     

Canonical Correlation Analysis Through Linear Modeling

In this paper, we introduce linear modeling of canonical correlation analysis, which estimates canonical direction matrices by minimising a quadratic objective function. The linear modeling results in a class of estimators of canonical direction matrices, and an optimal class is derived in the sense described herein. The optimal class guarantees several of the following desirable advantages: first, its estimates of canonical direction matrices are asymptotically efficient; second, its test statistic for determining the number of canonical covariates always has a chi‐squared distribution asymptotically; third, it is straight forward to construct tests for variable selection. The standard canonical correlation analysis and other existing methods turn out to be suboptimal members of the class. Finally, we study the role of canonical variates as a means of dimension reduction for predictors and responses in multivariate regression. Numerical studies and data analysis are presented.     

The Loss Rank Criterion for Variable Selection in Linear Regression Analysis

Abstract. Lasso and other regularization procedures are attractive methods for variable selection, subject to a proper choice of shrinkage parameter. Given a set of potential subsets produced by a regularization algorithm, a consistent model selection criterion is proposed to select the best one among this preselected set. The approach leads to a fast and efficient procedure for variable selection, especially in high‐dimensional settings. Model selection consistency of the suggested criterion is proven when the number of covariates d is fixed. Simulation studies suggest that the criterion still enjoys model selection consistency when d is much larger than the sample size. The simulations also show that our approach for variable selection works surprisingly well in comparison with existing competitors. The method is also applied to a real data set.     

Do the rich get digitally richer? Quantity and quality of support for digital engagement

This paper asks what predicts having access to and using social support networks that might help an individual in using the Internet. Following the course taken by the digital divide or digital inclusion research, this paper uses socio-cultural, socio-economic, social, and digital indicators to predict access to and the type of potential and actual social support networks that might help an individual in using the Internet. In addition, the paper examines the quality of the support received which is neglected in most investigations that mainly focus on quantitative indicators of support. The study draws on a representative survey conducted in the Netherlands; 1149 responses were obtained. The results show that while there are no real inequalities in access to and use of support, the quality of the support that people access is unequally distributed replicating existing patterns of disadvantage. Thus, access to support is another level at which the digital divide manifests and strengthens itself. Those who experience most problems online also seem to have the most difficulty obtaining high-quality support even when it is available, creating an even larger ‘gap’ between those who do and do not need support.     

Modeling Longitudinal Obesity Data with Intermittent Missingness Using a New Latent Variable Model

We propose a latent variable model for informative missingness in longitudinal studies which is an extension of latent dropout class model. In our model, the value of the latent variable is affected by the missingness pattern and it is also used as a covariate in modeling the longitudinal response. So the latent variable links the longitudinal response and the missingness process. In our model, the latent variable is continuous instead of categorical and we assume that it is from a normal distribution. The EM algorithm is used to obtain the estimates of the parameter we are interested in and Gauss–Hermite quadrature is used to approximate the integration of the latent variable. The standard errors of the parameter estimates can be obtained from the bootstrap method or from the inverse of the Fisher information matrix of the final marginal likelihood. Comparisons are made to the mixed model and complete-case analysis in terms of a clinical trial dataset, which is Weight Gain Prevention among Women (WGPW) study. We use the generalized Pearson residuals to assess the fit of the proposed latent variable model.     

指数母函数在数学期望计算中的应用

讨论指数母函数的性质，利用指数母函数得到几个结论的新证明；给出利用指数母函数计算随机变量数学期望的方法．利用这种方法，比直接利用数学期望和方差的定义计算更加简易．     

Variable selection for varying dispersion beta regression model

The beta regression models are commonly used by practitioners to model variables that assume values in the standard unit interval (0, 1). In this paper, we consider the issue of variable selection for beta regression models with varying dispersion (VBRM), in which both the mean and the dispersion depend upon predictor variables. Based on a penalized likelihood method, the consistency and the oracle property of the penalized estimators are established. Following the coordinate descent algorithm idea of generalized linear models, we develop new variable selection procedure for the VBRM, which can efficiently simultaneously estimate and select important variables in both mean model and dispersion model. Simulation studies and body fat data analysis are presented to illustrate the proposed methods.