一种非参数可加模型回归估计的简便方法

文章利用非参数可加模型来构造经济增长函数.从研究方法看,文章采用了一种较为简便的非参回归估计方法构造矩阵,并运用Backfitting算法来对该函数进行迭代求解.该方法简单明了,在程序实现过程中避免了选择窗宽的繁冗过程;从实证的角度看,文章从两个方面来考虑经济增长的影响因素:外商直接投资和普通高校招生情况,这两方面皆被目前人们所普遍关注.实证结果揭示了影响经济增长的一些重要特征.     

计量经济学和统计学视角下的线性回归模型——再议线性回归模型的设定

从属性、构建方法及意义等方面,分析研究线性回归模型在计量经济学和统计学两学科视角下的差异,并根据这种差异进一步提出回归模型的基本设定思路。研究表明:识别这种差异是完成模型设定工作的基础性和必要性举措,有助于实现线性回归模型的正确设定。以经典例证对计量经济学和统计学回归模型在应用中的区别以及模型设定问题进行进一步展示和分析。     

基于偏最小二乘方法的信用评分模型

为了克服信用评分模型中自变量存在多重共线性的问题,文章引入了偏最小二乘思想,即采用限制预测值的偏最小二乘回归和偏最小二乘Logistic回归来创建信用评分模型。偏最小二乘法可以同时解释因变量和自变量的变异,在实际运用中更加符合信用评分模型的特点。实证研究的结果表明,利用这两种偏最小二乘模型创建的信用评分模型具有很好的准确性和稳定性。     

带约束非线性回归模型参数的广义LS估计的存在性

文章利用非参数估计的思想对带约束条件非线性回归模型进行了随机化改造,将带约束条件非线性回归模型转化为一个不带约束条件的随机化模型。并运用这一随机化方法和最优化理论,在这个随机化模型下对带约束条件回归模型的广义LS估计量的存在性进行了研究。     

分位数回归在非寿险产品费率厘定中的应用

文章首先分析了非寿险产品费率厘定中的零索赔额现象;指出了线性回归模型和广义线性模型在非寿险产品费率厘定中存在的问题和不足;分析了分位数回归模型在非寿险产品费率厘定中的优点,并结合实例,给出了实证分析.结果表明,分位数回归模型更能从整体上反映出费率厘定变量之间的关系及其对索赔额的影响.     

SPSS软件教学中的两个问题研究

随着信息时代的到来,统计分析与统计分析软件的结合将成为必然。本文就SPSS统计分析软件在与多元线性回归模型建模及检验的结上以案例形式进行了阐述,并针对学生在学习SPSS数据文件转置时的困难,提出了解决办法。     

试论模糊回归与统计回归

统计回归的研究具有悠久的历史,在理论方面取得了丰硕的成果,在社会科学和自然科学等许多领域得到了广泛应用;模糊回归的研究时间较短,但在许多领域具有良好的应用前景.文章从模型假设和估计方法两个方面,将统计回归与模糊回归进行比较,分析了二者的不同之处,同时指出,模糊回归的研究可以借鉴统计回归的相关思想.     

金融区间数据的动态回归模型比较与实证检验

传统金融时间序列中,对于股价研究多以当日收盘价为基础。这样不可避免地会产生观测信息的损失,从而导致模型解释能力的降低。文章讨论了模糊自回归(FAR(p))模型和模糊双线性回归(FDLR(p,q))模型结构,并在金融动态数据不同趋势条件下,直接讨论针对区间序列的金融时间序列模型的变化;基于不同特点的金融区间波段,对这两个模型作了比较研究,进一步讨论了模型的拟合评价与解释能力。     

中国人口增长预测模型及其改进

文章根据已有的人口数据,运用数据拟合方法和矩阵方法分析研究了人口增长的规律,分别建立了预测中国人口增长的回归和凯菲茨矩阵两个数学模型,并且给出了中国人口短期和长期的预测结果.通过比较,阐述了两种模型的优缺点.     

基于时空地理加权回归模型的中国碳排放驱动因素实证研究

基于2006-2011年中国省级区域面板数据,应用时空加权回归模型(GTWR)实证考察了各驱动因素对碳排放规模和碳排放强度影响的时空差异。研究结果表明:大部分解释变量的时空系数估计值显著,波动性较为稳定,符号与预期一致,各驱动因素及其外溢效应在不同区域存在较强的空间异质性,且表现出一定的空间梯度分布。若实现区域差异化碳减排,需要充分考虑空间异质性和外溢性。     

Statistical Modeling for the Evaluation of Consumers’ Preferences

In 2009 a survey was performed in Veneto, a region in the north-east of Italy, to study the demand for wine and specifically for Passito, a typical Italian wine. The main goal of the study consisted in analyzing how the preferences and consumption habits of Passito vary depending on consumers’ characteristics. Specifically two kinds of statistical methods were applied: Covariate Uniform Binomial (CUB) model, a statistical approach for ordinal data to study the feeling toward Passito and the uncertainty of the respondents; classical logistic regression analysis, to describe how the attitude toward passito can be modeled as function of consumers’ covariates. Gender and residence were the most important covariates, useful in defining segments of consumers with significant differences in terms of Passito's preferences and consumption behavior. The logistic regression analysis allowed to complete the statistical analysis based on CUB models validating the results of the CUB model and estimating a model useful to predict the attitude toward the considered product for specific sub-groups of consumers.     

农村居民生活满意度的影响因素分析

 本文将Logistic回归方法运用于生活满意度影响因素的分析,首先根据国内外研究成果选取五个影响因素并提出研究假设,其次运用无序多分类Logistic回归模型拟合调查数据,科学确定各因素的影响程度。研究发现家庭年收入、居住区域和对社会公平的看法这三个因素对江苏农村居民生活满意度有显著影响。因此,以经济持续稳定增长促进农村居民持续增收,以社会公平为目标推进政治经济建设将成为提高江苏农村居民生活满意度的有效措施。     

Expected estimating equations to accommodate covariate measurement error

Estimating equations which are not necessarily likelihood-based score equations are becoming increasingly popular for estimating regression model parameters. This paper is concerned with estimation based on general estimating equations when true covariate data are missing for all the study subjects, but surrogate or mismeasured covariates are available instead. The method is motivated by the covariate measurement error problem in marginal or partly conditional regression of longitudinal data. We propose to base estimation on the expectation of the complete data estimating equation conditioned on available data. The regression parameters and other nuisance parameters are estimated simultaneously by solving the resulting estimating equations. The expected estimating equation (EEE) estimator is equal to the maximum likelihood estimator if the complete data scores are likelihood scores and conditioning is with respect to all the available data. A pseudo-EEE estimator, which requires less computation, is also investigated. Asymptotic distribution theory is derived. Small sample simulations are conducted when the error process is an order 1 autoregressive model. Regression calibration is extended to this setting and compared with the EEE approach. We demonstrate the methods on data from a longitudinal study of the relationship between childhood growth and adult obesity.     

基于多源数据融合的个人信用评分研究

随着信息技术的发展,数据来源越来越多,一方面可以更加精准、科学地刻画个人信用状况,但另一方面,由于数据来源多、结构复杂等问题,对传统的征信技术带来了挑战。本文提出了基于多源数据融合的个人信用模型,可以同时对多个数据集进行建模和变量选择,同时考虑了数据集间的相似性和异质性。通过模拟实验发现,本文所提出的整合模型在变量选择和分类效果方面都具有明显的优势。最后,将整合模型应用于城市和农村两个数据集的个人信用评分中。     

A generalization of the logistic linear'model

Consider the logistic linear model, with some explanatory variables overlooked. Those explanatory variables may be quantitative or qualitative. In either case, the resulting true response variable is not a binomial or a beta-binomial but a sum of binomials. Hence, standard computer packages for logistic regression can be inappropriate even if an overdispersion factor is incorporated. Therefore, a discrete exponential family assumption is considered to broaden the class of sampling models. Likelihood and Bayesian analyses are discussed. Bayesian computation techniques such as Laplacian approximations and Markov chain simulations are used to compute posterior densities and moments. Approximate conditional distributions are derived and are shown to be accurate. The Markov chain simulations are performed effectively to calculate posterior moments by using the approximate conditional distributions. The methodology is applied to Keeler's hardness of winter wheat data for checking binomial assumptions and to Matsumura's Accounting exams data for detailed likelihood and Bayesian analyses.     

Ten Suggestions for Effectively Teaching Short Courses to Heterogeneous Groups

Consider teaching a three-day short course in modern regression methodology to a small group consisting of engineers, social scientists, managers (who often have a business background), and medical researchers. Teaching such a course offers a different set of problems and challenges than encountered when teaching a course in the university setting. The instructor must be highly organized, well prepared, and flexible for the successful presentation of an intense short course. Ten suggestions are given that will increase the likelihood that the course will meet the educational objectives of such a diverse audience.     

The application of generalized ridge and stein-like general minimax rules to multicollinear data

We consider the use of minimax shrinkage estimators for the linear regression mcjel under several loss functions when severe multicollinearity is present. The examples considered illustrate that little or no departure from the least squares estimates is permitted in many cases when the data is highly multicollinear and/or shrinkage is toward a point in the parameter space that does not closely agree with the sample data     

Coversge probabilty to the original data by prediction intervals in simple linear regression

The coverage rate of the original data by the prediction interval in simple linear regression is obtained by computer simulation. The results show that for small sample size, the coverage rate is higher than the assigned prediction coverage rate (confidence level). The two coverage rates begin to converge when the sample size is larger than 50 and the convergence rate depends very little on the distribution of the independent variable. Also, theoretical results on the asymptotic coverage rate and on the absolute minimum bounds are obtained     

An em algorithm for a semiparametric finite mixture model

We propose a semiparametric version of the EM algorithm under the semiparametric mixture model introduced by Anderson (1979, Biometrika , 66 , 17-26). It is shown that the sequence of proposed EM iterates, irrespective of the starting value, converges to the maximum semiparametric likelihood estimator of the vector of parameters in the semiparametric mixture model. The proposed EM algorithm preserves the appealing monotone convergence property of the standard EM algorithm and can be implemented by employing the standard logistic regression program. We present one example to demonstrate the performance of the proposed EM algorithm.     

More Efficient Tests Robust to Heteroskedasticity of Unknown Form

In the presence of heteroskedasticity of unknown form, the Ordinary Least Squares parameter estimator becomes inefficient, and its covariance matrix estimator inconsistent. Eicker (1963) and White (1980) were the first to propose a robust consistent covariance matrix estimator, that permits asymptotically correct inference. This estimator is widely used in practice. Cragg (1983) proposed a more efficient estimator, but concluded that tests basd on it are unreliable. Thus, this last estimator has not been used in practice. This article is concerned with finite sample properties of tests robust to heteroskedasticity of unknown form. Our results suggest that reliable and more efficient tests can be obtained with the Cragg estimators in small samples.