中国居民储蓄的影响因素分析

文章在对居民储蓄影响因素进行分析的基础上,利用相关经济变量1978~2009年的时间序列数据,基于协整理论和误差修正模型,对我国城乡居民储蓄及其影响因素之间的长、短期关系进行了研究。结果表明,收入对居民储蓄的长期和短期影响均为最大;利率和通货膨胀率在长期对居民储蓄有影响,分别为正向和负向影响,但影响程度较小,短期无影响;预防性动机对居民储蓄的影响不显著。     

我国寿险需求影响因素的回归分析

本文运用1985-2005年的有关数据,对影响我国寿险需求的相关因素进行回归分析。结果表明:国民收入、赡养率和寿险供给对于我国寿险需求的影响显著;而名义利率和预期的通货膨胀率对我国寿险需求的影响不显著。     

对考试焦虑状况的Logistic回归模型分析

文章通过自编的考试焦虑自我检验问卷对在校大学生进行了抽样调查,并应用聚类分析和Logistic回归分析对影响考试焦虑的原因进行探讨。得出影响因素主要有担心他人对自己的评价,担心个人未来前途,担心个人对考试准备不足三个方面。     

基于偏最小二乘回归分析的农民收入影响因素研究

文章运用偏最小二乘(PLS)回归方法,分析了转轨以来影响农民增收的12个因素。研究表明,城市化率、农村工业化程度、农户受教育程度以及劳务经济对农民增收作用最为明显。基于以上分析结论,本文认为,加快城市化进程、大力发展乡镇企业以及提高农民文化素质是增加农民收入的根本途径。     

企业信用状况的定性评价——基于logistic回归模型的分析

以材料和机械制造行业100家上市公司综合财务数据为样本数据,采用主成分分析和logistic回归模型,对企业的信用风险进行定性评价,简要评定企业的守信状况,影响企业信用的主要是企业的偿债能力,且同资金的流动性和运营效果密切相关,并给出结论与建议,指导债权人、投资者和交易方投资决策。     

中国家庭养育目标的影响因素及其差异分析

基于2010年中国家庭动态跟踪调查(CFPS)数据库,分析养育目标在城乡之间以及不同少儿年龄组之间的差异,并利用Logistic回归模型探讨家庭养育目标的影响因素。结果表明:养育目标在城乡之间有显著差异,但在不同的少儿年龄组之间无显著差异;父亲职务、父母受教育程度及母亲户口状况不同程度地影响着"在自己年老时得到帮助"、"延续家族香火"、"从经济上帮助家庭"、"使家庭在自己的生活中更重要"和"增加亲属联系"这五个养育目标。     

Logistic回归模型在判别分析中的应用

介绍Logistic回归模型用于判别的方法,利用给出的某期间华北地区和长江中下游降水年变化为判别对象,以这种判别方法确定界于两个地区中间地带的一些观测站属于何种年变化型,并且与传统用的最大概率法做了比较,发现Logistic的效果要比最大概率法好。     

单因素方差分析与多元线性回归分析检验方法的比较

单因素方差分析与多元线性回归分析是应用统计学中两种重要的统计方法,文章通过严格的分析,找出了两者检验方法的异同点,并提出了应用时应注意的问题.     

基于分位数回归的居民人均收入实证研究

一、背景 为确保全面建成小康社会的目标,十八大明确提出了城乡居民人均收入倍增的目标,这项民生政策一出台就引起了广泛关注。从人均GDP到人均收入,这不仅仅是指标的“量”的变化,更是人均社会财富衡量方式的一种“质”的转变。只有着实提高人均可支配的收入才能让人民生活水平有“看得见”的提高。但,影响居民人均收入的因素有哪些？这些因素的作用过程都是一成不变的吗？如何定位以及控制这些因素呢？这些都是提高居民人均收入的过程中必须解决的问题。     

基于主成分回归的积极老龄化影响因素统计分析

文章通过主成分回归分析了解影响个体积极老龄化的因素,通过对比分析城乡居民和不同年龄段居民的积极老龄化影响因素,指出安徽省居民的自我养老意愿仍然较低,农村居民比城市居民的“养儿防老”观念更强,50～59岁的新成长老年人存在“被自我养老”的现象,60岁以上的老年人自我养老意愿“城乡差别”更大.最后从实现最佳化的健康、参与和保障的机会的视角构建积极老龄化的三大支柱.     

判别分析与Logistic回归的模拟比较

利用随机模拟方法,研究判别分析和Logistic回归分类的回判正确率。模拟结果显示,Logistic回归的回判正确率优于判别分析。随着随机误差的增大,Logistic回归与判别分析的回判正确率差异逐渐减小。随机误差超过一定界限,Logistic回归的回判正确率低于判别分析。在随机模拟的基础上,引入修正Logistic回归分类,模拟结果显示,修正Logistic回归分类略优于Logistic回归。     

Logistic Regression and Discriminant Analysis by Ordinary Least Squares

If the observations for fitting a polytomous logistic regression model satisfy certain normality assumptions, the maximum likelihood estimates of the regression coefficients are the discriminant function estimates. This article shows that these estimates, their unbiased counterparts, and associated test statistics for variable selection can be calculated using ordinary least squares regression techniques, thereby providing a convenient method for fitting logistic regression models in the normal case. Evidence is given indicating that the discriminant function estimates and test statistics merit wider use in nonnormal cases, especially in exploratory work on large data sets.     

Prediction in Multilevel Logistic Regression

The purpose of this article is to present a new method to predict the response variable of an observation in a new cluster for a multilevel logistic regression. The central idea is based on the empirical best estimator for the random effect. Two estimation methods for multilevel model are compared: penalized quasi-likelihood and Gauss–Hermite quadrature. The performance measures for the prediction of the probability for a new cluster observation of the multilevel logistic model in comparison with the usual logistic model are examined through simulations and an application.     

Logistic回归的双层变量选择研究

变量选择是统计建模的重要环节,选择合适的变量可以建立结构简单、预测精准的稳健模型。本文在logistic回归下提出了新的双层变量选择惩罚方法——adaptive Sparse Group Lasso(adSGL),其独特之处在于基于变量的分组结构作筛选,实现了组内和组间双层选择。该方法的优点是对各单个系数和组系数采取不同程度的惩罚,避免了过度惩罚大系数,从而提高了模型的估计和预测精度。求解的难点是惩罚似然函数不是严格凸的,因此本文基于组坐标下降法求解模型,并建立了调整参数的选取准则。模拟分析表明,对比现有代表性方法Sparse Group Lasso、Group Lasso及Lasso,adSGL法不仅提高了双层选择精度,而且降低了模型误差。最后本文将adSGL法应用到信用卡信用评分研究,对比logistic回归,它具有更高的分类精度和稳健性。     

Robust Principal Component Functional Logistic Regression

In this article, we discuss the estimation of the parameter function for a functional logistic regression model in the presence of outliers. We consider ways that allow for the parameter estimator to be resistant to outliers, in addition to minimizing multicollinearity and reducing the high dimensionality, which is inherent with functional data. To achieve this, the functional covariates and functional parameter of the model are approximated in a finite-dimensional space generated by an appropriate basis. This approach reduces the functional model to a standard multiple logistic model with highly collinear covariates and potential high-dimensionality issues. The proposed estimator tackles these issues and also minimizes the effect of functional outliers. Results from a simulation study and a real world example are also presented to illustrate the performance of the proposed estimator.     

On Ridge Parameters in Logistic Regression

This article applies and investigates a number of logistic ridge regression (RR) parameters that are estimable by using the maximum likelihood (ML) method. By conducting an extensive Monte Carlo study, the performances of ML and logistic RR are investigated in the presence of multicollinearity and under different conditions. The simulation study evaluates a number of methods of estimating the RR parameter k that has recently been developed for use in linear regression analysis. The results from the simulation study show that there is at least one RR estimator that has a lower mean squared error (MSE) than the ML method for all the different evaluated situations.     

Jackknife-After-Bootstrap as Logistic Regression Diagnostic Tool

In this study, we propose using Jackknife-after-Bootstrap (JaB) method to detect influential observations in binary logistic regression model. Performance of the proposed method has been compared with the traditional method for standardized Pearson residuals, Cook's distance, change in the Pearson chi-square and change in the deviance statistics by both real world examples and simulation studies. The results reveal that under the various scenarios considered in this article, JaB performs better than the traditional method and is more robust to masking effect especially for Cook's distance.     

Identification of Multiple Outliers in Logistic Regression

The use of logistic regression modeling has seen a great deal of attention in the literature in recent years. This includes all aspects of the logistic regression model including the identification of outliers. A variety of methods for the identification of outliers, such as the standardized Pearson residuals, are now available in the literature. These methods, however, are successful only if the data contain a single outlier. In the presence of multiple outliers in the data, which is often the case in practice, these methods fail to detect the outliers. This is due to the well-known problems of masking (false negative) and swamping (false positive) effects. In this article, we propose a new method for the identification of multiple outliers in logistic regression. We develop a generalized version of standardized Pearson residuals based on group deletion and then propose a technique for identifying multiple outliers. The performance of the proposed method is then investigated through several examples.     

两类分析相关数据的Logistic回归模型

摘　　要:本文主要结合具体例子,针对具有相关关系的分类数据的统计分析,介绍了两类Logistic回归模型,并分析了它们的联系与区别 。