Clusterwise Multivariate Kernel Ridge Regression Analysis with an Application

2021 ◽  
Vol 50 (1) ◽  
pp. 1-20
Author(s):  
Yuta Kawakami ◽  
Manabu Kuroki
Keyword(s):  
Regression Analysis ◽  
Ridge Regression ◽  
Kernel Ridge Regression ◽  
Ridge Regression Analysis

scholarly journals How Do the Population Structure Changes of China Affect Carbon Emissions? An Empirical Study Based on Ridge Regression Analysis

2021 ◽  
Vol 13 (6) ◽  
pp. 3319
Author(s):  
Chulin Pan ◽  
Huayi Wang ◽  
Hongpeng Guo ◽  
Hong Pan
Keyword(s):  
Population Structure ◽  
Regression Analysis ◽  
Population Size ◽  
Carbon Emissions ◽  
Ridge Regression ◽  
Employment Structure ◽  
Consumption Structure ◽  
Structure Changes ◽  
Size Population ◽  
Ridge Regression Analysis

This study focuses on the impact of population structure changes on carbon emissions in China from 1995 to 2018. This paper constructs the multiple regression model and uses the ridge regression to analyze the relationship between population structure changes and carbon emissions from four aspects: population size, population age structure, population consumption structure, and population employment structure. The results showed that these four variables all had a significant impact on carbon emissions in China. The ridge regression analysis confirmed that the population size, population age structure, and population employment structure promoted the increase in carbon emissions, and their contribution ratios were 3.316%, 2.468%, 1.280%, respectively. However, the influence of population consumption structure (−0.667%) on carbon emissions was negative. The results showed that the population size had the greatest impact on carbon emissions, which was the main driving factor of carbon emissions in China. Chinese population will bring huge pressure on the environment and resources in the future. Therefore, based on the comprehensive analysis, implementing the one-child policy will help slow down China’s population growth, control the number of populations, optimize the population structure, so as to reduce carbon emissions. In terms of employment structure and consumption structure, we should strengthen policy guidance and market incentives, raising people’s low-carbon awareness, optimizing energy-consumption structure, improving energy efficiency, so as to effectively control China’s carbon emissions.

Parameter Estimation in Marketing Models in the Presence of Multicollinearity: An Application of Ridge Regression

1977 ◽  
Vol 14 (4) ◽  
pp. 586-591 ◽  
Author(s):  
Vijay Mahajan ◽  
Arun K. Jain ◽  
Michel Bergier
Keyword(s):  
Parameter Estimation ◽  
Regression Analysis ◽  
Least Squares ◽  
Ridge Regression ◽  
Ordinary Least Squares ◽  
Regression Coefficients ◽  
Estimation Of Parameters ◽  
Marketing Models ◽  
Marketing Data ◽  
Ridge Regression Analysis

In the presence of multicollinearity in data, the estimation of parameters or regression coefficients in marketing models by means of ordinary least squares may give inflated estimates with a high variance and wrong signs. The authors demonstrate the potential usefulness of the ridge regression analysis to handle multicollinearity in marketing data.

Sign in / Sign up

Export Citation Format

Share Document