Statistical Estimates for Modified Gravitation Model by Aggregated Data

A nonlinear regression model for forecasting of passenger flow between various spatial points (towns) is described. Unknown parameters are estimated using aggregated data when the information about a number of the departed passengers from each town is available only. For estimation, the least squares and maximum likelihood methods are used. Numerical examples are performed to illustrate the proposed approaches.     

An Improved Estimation in Regression Parameter Matrix in Multivariate Regression Model

We consider shrinkage and preliminary test estimation strategies for the matrix of regression parameters in multivariate multiple regression model in the presence of a natural linear constraint. We suggest a shrinkage and preliminary test estimation strategies for the parameter matrix. The goal of this article is to critically examine the relative performances of these estimators in the direction of the subspace and candidate subspace restricted type estimators. Our analytical and numerical results show that the proposed shrinkage and preliminary test estimators perform better than the benchmark estimator under candidate subspace and beyond. The methods are also applied on a real data set for illustrative purposes.     

Estimating the error distribution function in semiparametric additive regression models

We consider semiparametric additive regression models with a linear parametric part and a nonparametric part, both involving multivariate covariates. For the nonparametric part we assume two models. In the first, the regression function is unspecified and smooth; in the second, the regression function is additive with smooth components. Depending on the model, the regression curve is estimated by suitable least squares methods. The resulting residual-based empirical distribution function is shown to differ from the error-based empirical distribution function by an additive expression, up to a uniformly negligible remainder term. This result implies a functional central limit theorem for the residual-based empirical distribution function. It is used to test for normal errors.     

Lack-of-fit testing of a regression model with response missing at random

This paper proposes a class of lack-of-fit tests for fitting a linear regression model when some response variables are missing at random. These tests are based on a class of minimum integrated square distances between a kernel type estimator of a regression function and the parametric regression function being fitted. These tests are shown to be consistent against a large class of fixed alternatives. The corresponding test statistics are shown to have asymptotic normal distributions under null hypothesis and a class of nonparametric local alternatives. Some simulation results are also presented.     

Kernel adjusted nonparametric regression

In this paper we propose and study a new kernel regression estimator in which the kernel is taken from a properly adapted location-scale family of the design distribution. We show that, while the original smoothing may be performed with sub-optimal bandwidths, adaptation of proper scale parameters yields overall optimal estimators. Unlike traditional smoothing methodology, our approach does not aim at estimating pivotal higher order derivatives.     

空间效率、外部性与区域收入差异研究

从空间效率和外部性的角度来探讨区域收入差异问题,以新经济地理学经典的工资方程为理论基础,利用中国2000-2009年30个省份的面板数据以及分位数回归的方法,检验了各种类型的市场潜能(金融外部性或空间效率)、就业密度(技术外部性)和地区工资的关系.研究发现:无论是全国样本还是分地区样本,市场潜能对地区工资的正向效应显著;在分样本中,就业密度外部性对地区工资具有“门槛”效应;除中西部地区,国外市场潜能(出口开放)和国内市场潜能之间具有明显的替代关系;不同分位点处,不同区域下的解释变量对于职工平均工资的影响具有鲜明的区域特征.     

非参数固定效应Panel Data模型的分位数回归推断

利用分位数回归方法,讨论了非参数固定效应Panel Data模型的估计和检验问题,得到了参数估计的渐近正态性及收敛速度。同时,建立一个秩得分(rank score)统计量来检验模型的固定效应,并证明了这个统计量渐近服从标准正态分布。     

SCAD惩罚逻辑回归的财务预警模型

作为一种有监督学习算法,逻辑回归(Logistic Regression,LR)已广泛应用于财务危机建模分析,但其潜在地存在过拟合问题。鉴此,提出一种基于平滑削边绝对偏离(Smoothly Clipped Absolute Deviation,SCAD)惩罚逻辑回归的财务预警模型。该模型不仅能很好地解决模型过拟合问题,而且还可以同时实现变量选择和模型系数估计,并提高了模型的解释性。结合沪深股市A股制造业上市公司的财务数据进行实证研究,同时对比一般的L1正则化和L2正则化逻辑回归模型的预警效果进行实证分析,实验结果表明:SCAD惩罚逻辑回归模型具有较好的分类效果和较强的经济解释能力。     

中国电动汽车配套基础设施布局需求侧研究——基于Logit回归的消费偏好分析

从消费者偏好角度出发,通过一系列实地访谈、问卷调研,获取需求侧第一手数据,综合利用因子分析及Logit回归方法,发现在电动汽车配套基础设施布局过程中,消费者比较重视社会环境效益、技术属性、内在条件及外在属性四大类因子.同时研究发现,消费者对汽车的消费意识也有所变化,从节能与减排的角度出发,消费者倾向于购买能源补给成本较低、环保效果更好的车型.在需求侧研究的基础上,立足中国电动汽车行业及其充电基础设施的实际情况,可从突破技术瓶颈、推广分散式充电桩、油电一体化加油、充换电站、充分考虑消费者偏好四个方面着手,进一步完善中国电动汽车配套基础设施布局,推动电动汽车的产业化和规模化.