基于Ordered Logistic模型的居民幸福感组群差异分析

文章以兰州市居民幸福感调查结果为数据源,应用Ordered Logistic模型对居民幸福感在若干变量上的组群差异进行了较系统的分析。结果表明,幸福感在性别、教育程度、婚姻状况、健康状况、家庭氛围、职业稳定变量上的组群差异显著,可以认为这些变量是影响当前兰州市居民幸福感的主要因素。提出了提高兰州市居民幸福感的政策建议。     

基于多重多元回归的多目标影响因素效应比较及应用——以企业经营活动分析为例

企业运营管理活动的结果可能表现在多个方面,这些结果的产生会受到若干个因素的影响。影响因素及其组合对企业运营管理结果的作用效果是否存在差异,从计量角度加以测算分析,有助于企业资源的合理配置和利用。运用统计回归分析的基本原理,讨论了多个影响因素及其组合对企业经营多个目标回归效应差异问题,包括:不同组解释变量对同一被解释变量回归效应的比较,同一组解释变量对不同被解释变量回归效应的比较,不同组解释变量对所有被解释变量回归效应的比较,并结合事实数据进行了应用说明。     

多源高维数据的多分类纵向整合分析及应用

多分类数据分析在实证研究中具有重要意义。然而,由于高维数、小样本及低信噪比等原因,现有的多分类方法仍面临信息量不足而导致的效果不佳问题。为此,学者们通过收集更多信息源 数据以更全面地刻画实际问题。不同于收集相同自变量的不同源样本,目前较为流行的多源数据收集了相同样本的不同源自变量,它们的独立性和相关性为统计建模带来了新的挑战。本文提出基于典型变量回归的多分类纵向整合分析方法,其中利用惩罚技术实现变量选择,并独特地考虑不同源数据间的关联结构,提出高效的ADMM算法进行模型优化。数值模拟结果表明,该方法在变量选择和分类预测 上均具有优越性。基于我国上证50的多源股票数据,利用该方法对2019年股票日收益率的影响因素进行了实证探究。研究表明,本文提出的多分类整合分析在筛选出具有解释意义变量的同时具有更好的预测效果。     

基于聚类分析的商业银行效率评价模型研究

我国商业银行的规模和财务指标存在较大差别,相应影响商业银行贷款效率的因素也存在较大差异。文章通过类平均聚类方法,将欧几里德距离较小且具有相似经济背景的银行分为一组,得到五大国有银行类及非国有银行类两类银行。通过逐步回归方法,逐步剔除SFA模型中t检验不显著的变量,保留所有对被解释变量影响显著的解释变量,建立商业银行贷款效率评价模型,并进行了实证分析。     

我国通货膨胀的主要经济影响因素分析与统计检验

文章首先分别就我国通货膨胀对每个经济影响因素(经济增长率、货币供应增长率、外汇储备增长率和全社会固定资产投资增长率和上一期的居民消费价格指数这五个因素)和通货膨胀率建立分布滞后模型,确定每个影响因素的最佳滞后期;接着将每个影响因素的最佳滞后期引入,建立以通货膨胀率(用居民消费价格指数衡量)为被解释变量,各个最佳滞后期的影响因素为解释变量的分布滞后模型;然后对这个分布滞后模型进行经济意义(包括是否符合经济规律)、统计意义(包括参数值和模型是否显著)以及计量经济意义上(包括多重共线性、异方差和自相关等)的检验,对初步的回归模型进行修正,获得最终的分布滞后模型;最后根据此模型进行结果分析,并根据回归结果得出结论.     

基于Shapley值的分类预测模型变量筛选方法改进

在分类预测模型的自变量间存在交互效应时，传统Shapley值法的可加性无法满足，造成变量筛选效果变差，导致分类模型的预测精度降低。针对此问题，文章提出使用稳健独立成分分析，从原始数据中估计出具有独立性的数据集并对其进行Shapley值分解，从而提高变量筛选的准确度。统计模拟与实证分析的结果表明，改进后的方法在变量筛选上的表现优于传统Shapley值法。     

基于Hsiao程序的最优解释变量的选择

文章首先对Hsiao程序的理论进行了介绍:然后,以人民币行为均衡汇率模型(BEER模型)为例,使用该程序进行解释变量选择.筛选得到的解释变量与国内外大多数学者利用该模型进行研究所选择的经济变量基本一致,这说明使用Hsiao程序选择解释变量是可信的.但是,也存在一些差异,主要因为样本选择的范围不同以及所使用的数据质量本身问题.文章的创新之处在于利用运筹学的最优选择思想,借助Hsiao程序.进行解释变量的选择,这对某些经济变量(缺乏决定因素的先验理论)进行回归分析时,有很大的参考价值,同时,可避免主观臆断.     

聚合信息客户端数据挖掘应用研究——以用户登录行为和文章推荐数据库为例

在大数据时代,数据挖掘技术在聚合信息客户端中的应用有利于提高聚合信息企业的运行效率。基于聚合信息企业的实际运营和用户数据,从用户登录行为和文章推荐数据库两个角度,利用机器学习算法,构建用户登录行为预测模型和优秀文章分类模型。研究发现,随机森林和Logistic回归模型在互联网大数据分析中的综合表现最好,在分类预测准确度和运行速度方面明显优于其他模型;用户对平台的使用频率和依赖度是决定其登录行为的最关键因素,且区域用户习惯和年龄显著影响用户的登录决策;文章基本信息和自媒体属性均对优秀文章筛选有显著影响,其中,文章等级、自媒体的产量和自媒体专注度等均与文章质量存在显著的负相关关系;发文类型、是否原创和自媒体领域等分类变量各水平之间都存在显著差异,且均会影响用户对文章的青睐程度。     

主成分分析与因子分析在实践应用中的辨析

在社会经济实证问题研究中。为全面、系统地分析问题,通常要考虑众多对某经济过程有影响的因素,而太多的因素变量会增大计算工作量和增加分析问题的复杂性。但是盲目的减少指标会损失很多信息,容易产生错误的结论。主成分分析是研究如何通过少数几个主成分来解释多变量的方差—协方差结构的分析方法,也就是求出少数几个主成分,使它们尽可能多地保     

持续经营能力重大不确定性下审计意见的预测研究

持续经营审计意见的出具属于多阶段决策问题,本文采用多分类Logistic回归模型,以131家上市公司为样本,对影响持续经营审计意见类型的因素进行了分析。提出了一个三分类Logistic回归模型,该模型对持续经营审计意见类型的预测准确率为83.2%,显示了良好的预测能力。     

Robust multivariate mixture regression models with incomplete data

Multivariate mixture regression models can be used to investigate the relationships between two or more response variables and a set of predictor variables by taking into consideration unobserved population heterogeneity. It is common to take multivariate normal distributions as mixing components, but this mixing model is sensitive to heavy-tailed errors and outliers. Although normal mixture models can approximate any distribution in principle, the number of components needed to account for heavy-tailed distributions can be very large. Mixture regression models based on the multivariate t distributions can be considered as a robust alternative approach. Missing data are inevitable in many situations and parameter estimates could be biased if the missing values are not handled properly. In this paper, we propose a multivariate t mixture regression model with missing information to model heterogeneity in regression function in the presence of outliers and missing values. Along with the robust parameter estimation, our proposed method can be used for (i) visualization of the partial correlation between response variables across latent classes and heterogeneous regressions, and (ii) outlier detection and robust clustering even under the presence of missing values. We also propose a multivariate t mixture regression model using MM-estimation with missing information that is robust to high-leverage outliers. The proposed methodologies are illustrated through simulation studies and real data analysis.     

A class of residuals for outlier identification in zero adjusted regression models

Zero adjusted regression models are used to fit variables that are discrete at zero and continuous at some interval of the positive real numbers. Diagnostic analysis in these models is usually performed using the randomized quantile residual, which is useful for checking the overall adequacy of a zero adjusted regression model. However, it may fail to identify some outliers. In this work, we introduce a class of residuals for outlier identification in zero adjusted regression models. Monte Carlo simulation studies and two applications suggest that one of the residuals of the class introduced here has good properties and detects outliers that are not identified by the randomized quantile residual.     

Pseudo–Modelling of Outliers for Estimation of Regression And Time Series Models

This paper documents situations where the variance inflation model for outliers has undesirable properties. The model is commonly used to accommodate outliers in a Bayesian analysis of regression and time series models. The alternative approach provided here does not suffer from these undesirable properties but gives inferences similar to those of the variance inflation model when this is appropriate. It can be used with regression, time series, and regression with correlated errors in a unified way, and adheres to the scientific principle that inference should be based on the data after obvious outliers have been discarded. Only one parameter is required for outliers; it is interpretable as the a priori willingness to remove observations from the analysis.     

Two Moments of the Logitnormal Distribution

We display the first two moment functions of the Logitnormal(μ, σ²) family of distributions, conveniently described in terms of the Normal mean, μ, and the Normal signal-to-noise ratio, μ/σ, parameters that generate the family. Long neglected on account of the numerical integrations required to compute them, awareness of these moment functions should aid the sensible interpretation of logistic regression statistics and the specification of “diffuse” prior distributions in hierarchical models, which can be deceiving. We also use numerical integration to compare the correlation between bivariate Logitnormal variables with the correlation between the bivariate Normal variables from which they are transformed.     

Robust mixture regression modeling using the least trimmed squares (LTS)-estimation method

Mixture regression models are used to investigate the relationship between variables that come from unknown latent groups and to model heterogenous datasets. In general, the error terms are assumed to be normal in the mixture regression model. However, the estimators under normality assumption are sensitive to the outliers. In this article, we introduce a robust mixture regression procedure based on the LTS-estimation method to combat with the outliers in the data. We give a simulation study and a real data example to illustrate the performance of the proposed estimators over the counterparts in terms of dealing with outliers.     

Fitting multiplicative models by robust alternating regressions

In this paper a robust approach for fitting multiplicative models is presented. Focus is on the factor analysis model, where we will estimate factor loadings and scores by a robust alternating regression algorithm. The approach is highly robust, and also works well when there are more variables than observations. The technique yields a robust biplot, depicting the interaction structure between individuals and variables. This biplot is not predetermined by outliers, which can be retrieved from the residual plot. Also provided is an accompanying robust R ²-plot to determine the appropriate number of factors. The approach is illustrated by real and artificial examples and compared with factor analysis based on robust covariance matrix estimators. The same estimation technique can fit models with both additive and multiplicative effects (FANOVA models) to two-way tables, thereby extending the median polish technique.     

Bayesian composite quantile regression for linear mixed-effects models

Longitudinal data are commonly modeled with the normal mixed-effects models. Most modeling methods are based on traditional mean regression, which results in non robust estimation when suffering extreme values or outliers. Median regression is also not a best choice to estimation especially for non normal errors. Compared to conventional modeling methods, composite quantile regression can provide robust estimation results even for non normal errors. In this paper, based on a so-called pseudo composite asymmetric Laplace distribution (PCALD), we develop a Bayesian treatment to composite quantile regression for mixed-effects models. Furthermore, with the location-scale mixture representation of the PCALD, we establish a Bayesian hierarchical model and achieve the posterior inference of all unknown parameters and latent variables using Markov Chain Monte Carlo (MCMC) method. Finally, this newly developed procedure is illustrated by some Monte Carlo simulations and a case analysis of HIV/AIDS clinical data set.     

Diagnostics in multivariate generalized Birnbaum-Saunders regression models

Birnbaum–Saunders (BS) models are receiving considerable attention in the literature. Multivariate regression models are a useful tool of the multivariate analysis, which takes into account the correlation between variables. Diagnostic analysis is an important aspect to be considered in the statistical modeling. In this paper, we formulate multivariate generalized BS regression models and carry out a diagnostic analysis for these models. We consider the Mahalanobis distance as a global influence measure to detect multivariate outliers and use it for evaluating the adequacy of the distributional assumption. We also consider the local influence approach and study how a perturbation may impact on the estimation of model parameters. We implement the obtained results in the R software, which are illustrated with real-world multivariate data to show their potential applications.     

Robust estimators for additive models using backfitting

Additive models provide an attractive setup to estimate regression functions in a nonparametric context. They provide a flexible and interpretable model, where each regression function depends only on a single explanatory variable and can be estimated at an optimal univariate rate. Most estimation procedures for these models are highly sensitive to the presence of even a small proportion of outliers in the data. In this paper, we show that a relatively simple robust version of the backfitting algorithm (consisting of using robust local polynomial smoothers) corresponds to the solution of a well-defined optimisation problem. This formulation allows us to find mild conditions to show Fisher consistency and to study the convergence of the algorithm. Our numerical experiments show that the resulting estimators have good robustness and efficiency properties. We illustrate the use of these estimators on a real data set where the robust fit reveals the presence of influential outliers.     

Mixtures of regression models with incomplete and noisy data

The estimation of the mixtures of regression models is usually based on the normal assumption of components and maximum likelihood estimation of the normal components is sensitive to noise, outliers, or high-leverage points. Missing values are inevitable in many situations and parameter estimates could be biased if the missing values are not handled properly. In this article, we propose the mixtures of regression models for contaminated incomplete heterogeneous data. The proposed models provide robust estimates of regression coefficients varying across latent subgroups even under the presence of missing values. The methodology is illustrated through simulation studies and a real data analysis.