Robust variable selection and parametric component identification in varying coefficient models

ABSTRACT

In this paper, we study a novelly robust variable selection and parametric component identification simultaneously in varying coefficient models. The proposed estimator is based on spline approximation and two smoothly clipped absolute deviation (SCAD) penalties through rank regression, which is robust with respect to heavy-tailed errors or outliers in the response. Furthermore, when the tuning parameter is chosen by modified BIC criterion, we show that the proposed procedure is consistent both in variable selection and the separation of varying and constant coefficients. In addition, the estimators of varying coefficients possess the optimal convergence rate under some assumptions, and the estimators of constant coefficients have the same asymptotic distribution as their counterparts obtained when the true model is known. Simulation studies and a real data example are undertaken to assess the finite sample performance of the proposed variable selection procedure.     

河北三次产业运行的实证研究

主要立足于宏观经济系统四个变量之间是否存在格兰杰因果关系的分析,了解这些变量间的制约或促进关系.基于系统的向量自回归(VAR)上的格兰杰因果关系分析,同时结合经济学理论,提出相应的政策建议.     

宏观经济计量模型

本文根据我国宏观经济的原则——“四大平衡”,建立宏观经济计量模型,该模型共有20个内生变量、18个外生变量和一个虚拟变量,且由12个回归方程和8个定义方程所组成,经过统计检验分析,证明该模型比较接近实际情形.     

人群工作行为定性模拟方法

依据QSIM算法的思路,提出了一种对人群工作行为特征的变化过程进行预测和描述的定性模拟方法.设计了人群工作行为特征及其相互作用关系的定性表示法,提出了决策变量和状态变量的概念;给出状态转换规则,在此基础上列举了所有可能的I转换和P转换,并做了相应的图形解释;设计了算法步骤;最后对某人群工作行为变化过程进行了定性模拟,表明所提出的人群工作行为定性模拟方法能够较好地解释或预测人群工作行为的变化过程.     

多品种随机数学模型的物流配送中心选址问题

本文对照文[12],提出了一个多品种随机化的模型,并从数学角度对该模型进行了的一些分析,给出了单配送中心选址问题的一个量化的处理方法,当城市商品需求量服从指数分布或者帕累托分布时,我们的计算公式非常简单。     

基于知识和模糊神经网络的学习型评价系统

提出一种学习型评价系统的建立方法. 评价功能是基于决策者(专家) 的知识和模糊神经 网络实现的,适用于以语言型变量为主的系统的评价问题. 样本数据集的建立及语言型变量的 描述,是通过挖掘专家知识,建立符合其偏好的隶属函数实现的. 该评价系统可以充分利用以 往的决策案例,通过学习获取决策者的知识和经验,从而得到与决策者的评价结论相同的评价 结果.     

Scaling unobservable constructs in social science

The use of the logit–probit model for constructing social measurement scales is explored by using two data sets. The first was specifically designed for scaling and consists of answers by 1490 individuals to five questions on economic self-determination. The second is from the Workplace Industrial Relations Survey and relates to answers to six questions addressed to 1005 firms about innovations. It is shown that although scales can be constructed their precision (reliability) is likely to be low. Further, for examples like these, latent class models may give an equally satisfactory account of the data.     

中国的OFDI与“一带一路”沿线国家绿色发展

通过2004—2017年的国际面板数据,运用带非期望产出的SBM模型测算GML指数,用其衡量“一带一路”沿线31个国家的绿色发展水平,考察中国OFDI与“一带一路”沿线国家绿色发展之间的关系,检验“一带一路”国家是否成为中国的OFDI的“污染避难所”。结果表明:中国的OFDI显著促进了“一带一路”沿线国家绿色发展水平,通过工具变量处理潜在的内生性问题后结果仍然成立,验证了“一带一路”沿线国家并非是中国的OFDI“污染避难所”;分样本估计结果发现,中国的OFDI对亚洲国家的绿色全要素增长效应大于欧洲国家;通过引入基础设施水平和能源利用水平两个变量,发现基础设施水平和能源利用水平在中国的OFDI改善“一带一路”沿线国家绿色发展水平中起着显著的中介效应,说明中国的OFDI可以改善“一带一路”沿线国家的基础水平和能源利用水平,促进其绿色发展。     

AN EVALUATION OF NON-ITERATIVE METHODS FOR ESTIMATING THE LINEAR-BY-LINEAR PARAMETER OF ORDINAL LOG-LINEAR MODELS

Parameter estimation for association and log-linear models is an important aspect of the analysis of cross-classified categorical data. Classically, iterative procedures, including Newton's method and iterative scaling, have typically been used to calculate the maximum likelihood estimates of these parameters. An important special case occurs when the categorical variables are ordinal and this has received a considerable amount of attention for more than 20 years. This is because models for such cases involve the estimation of a parameter that quantifies the linear-by-linear association and is directly linked with the natural logarithm of the common odds ratio. The past five years has seen the development of non-iterative procedures for estimating the linear-by-linear parameter for ordinal log-linear models. Such procedures have been shown to lead to numerically equivalent estimates when compared with iterative, maximum likelihood estimates. Such procedures also enable the researcher to avoid some of the computational difficulties that commonly arise with iterative algorithms. This paper investigates and evaluates the performance of three non-iterative procedures for estimating this parameter by considering 14 contingency tables that have appeared in the statistical and allied literature. The estimation of the standard error of the association parameter is also considered.     

Analysis of rounded exponential data

The problem of inference based on a rounded random sample from the exponential distribution is treated. The main results are given by an explicit expression for the maximum-likelihood estimator, a confidence interval with a guaranteed level of confidence, and a conjugate class of distributions for Bayesian analysis. These results are exemplified on two concrete examples. The large and increasing body of results on the topic of grouped data has been mostly focused on the effect on the estimators. The methods and results for the derivation of confidence intervals here are hence of some general theoretical value as a model approach for other parametric models. The Bayesian credibility interval recommended in cases with a lack of other prior information follows by letting the prior equal the inverted exponential with a scale equal to one divided by the resolution. It is shown that this corresponds to the standard non-informative prior for the scale in the case of non-rounded data. For cases with the absence of explicit prior information it is argued that the inverted exponential prior with a scale given by the resolution is a reasonable choice for more general digitized scale families also.