摘要
近年来房地产业发展迅猛,房价快速走高,对经济发展和社会稳定产生了重大影响,因此房价研究具有重要的社会价值和经济意义.文章通过散点图及其拟合曲线展示不同协变量和响应变量之间的相关关系,提出半变系数模型建模美国埃姆斯市的房价问题.文章采用剖面最小二乘法研究7个协变量:地面以上的居住面积、地面以上的房间总数(不包括卫生间)、卫生间总数、房屋整体建材质量和品质、地下室面积、车库面积、建造年份与埃姆斯市房价之间的回归关系,为房价预测研究作准备.研究结果表明半变系数模型在探索美国爱荷华州埃姆斯市房价方面表现较好.因此,文章提供了一种利用半变系数模型同时学习高维数据线性和交互关系的有效方法.
In recent years,the real estate industry has developed rapidly and house prices have risen rapidly,which has an important impact on economic development and social stability.Therefore,the analysis for the housing prices problem possesses important social and economic significance.In this paper,we apply the scatter plots and the fitted curves to exhibit the correlation relationships between the different covariates and the response variable.We propose a semi-varying coefficient model to explore the housing price problem for Ames city of Iowa state in American,develop the profile least squares method to study the regression relationship between the housing price of Ames and the seven independent variables:GrLivArea,TotalRmsAbvGrd,Bath,OverallQual,TotalBsmtSF,GarageArea,YearBuilt,SalePrice,which are ready to predict the housing price.Our results show that semi-varying coefficient model performs well in exploring the housing price problem for Ames city of Iowa state in American.Therefore,in this paper we provide an efficient method for simultaneously studying linear relationships and interactive relationships for high dimensional data.
作者
胡雪梅
韦小凡
HU Xuemei;WEI Xiaofan(Chongqing Key Laboratory of Social Economy and Applied Statistics,Chongqing 400067;School of Mathematics and Statistics,Chongqing Technology and Business University,Chongqing 400067)
出处
《系统科学与数学》
CSCD
北大核心
2021年第1期269-279,共11页
Journal of Systems Science and Mathematical Sciences
基金
重庆市第五批高等学校优秀人才支持计划《基于分类方法预测股价的趋势运动》
重庆市科委基础研究与前沿探索一般项目(cstc.2018jcyjA2073)
重庆市“统计学”研究生导师团队(yds183002)
重庆市社会科学规划项目(2019BS061,2019WT59,2020YBTJ102)
社会经济应用统计重庆市重点实验室平台开放项目(KFJJ2018066)
重庆工商大学数理统计团队(ZDPTTD201906)
重庆工商大学高层次人才项目(1956003)资助课题。
关键词
半变系数模型
剖面最小二乘法
房价
Semi-varying coefficient model
profile least squares method
housing price
