Multicollinearity and financial constraint in investment decisions: a Bayesian generalized ridge regression

Kalatzis, Aquiles Elie Guimarães; Bassetto, Camila Fernanda; Azzoni, Carlos Roberto

doi:10.1080/02664760903406462

Cited by 6 publications

(1 citation statement)

References 42 publications

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“…Bayesian ridge regression (BRR) is a machine learning regression algorithm based on Bayesian theory [11,12]. Bayesian linear regression is shown in Equation (3).…”

Section: Bayesian Ridge Regressionmentioning

confidence: 99%

Hyperspectral Inversion of Soil Organic Matter Content Based on a Combined Spectral Index Model

Wei

Zhang

Wang

et al. 2020

Sensors

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Soil organic matter (SOM) refers to all carbon-containing organic matter in soil and is one of the most important indicators of soil fertility. The hyperspectral inversion analysis of SOM traditionally relies on laboratory chemical testing methods, which have the disadvantages of being inefficient and time-consuming. In this study, 69 soil samples were collected from the Honghu farmland area and a mining area in northwest China. After pretreatment, 10 spectral indicators were obtained. Ridge regression, kernel ridge regression, Bayesian ridge regression, and AdaBoost algorithms were then used to construct the SOM hyperspectral inversion model based on the characteristic bands, and the accuracy of the models was compared. The results showed that the AdaBoost algorithm based on a grid search had the best accuracy in the different regions. For the mining area in northwest China, R p 2 = 0.91, R M S E p = 0.22, and M A E p = 0.2. For the Honghu farmland area, R p 2 = 0.86, R M S E p = 0.72, and M A E p = 0.56. The detection of SOM content using hyperspectral technology has the characteristics of a high detection precision and high speed, which will be of great significance for the rapid development of precision agriculture.

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“…Bayesian ridge regression (BRR) is a machine learning regression algorithm based on Bayesian theory [11,12]. Bayesian linear regression is shown in Equation (3).…”

Section: Bayesian Ridge Regressionmentioning

confidence: 99%

Hyperspectral Inversion of Soil Organic Matter Content Based on a Combined Spectral Index Model

Wei

Zhang

Wang

et al. 2020

Sensors

View full text Add to dashboard Cite

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Impact of risk factors on the link between natural resources rents and carbon emissions: Evidence from economic, financial, and political risks

Wang,

Zhang,

2024

Humanit Soc Sci Commun

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Effective management of natural resources is crucial for diminishing carbon emissions. This research explores how economic, financial, and political risks influence the relationship between natural resources rents and carbon emissions. Analyzing data from 66 countries, this study utilizes methods such as quantile regression and dynamic threshold regression to thoroughly assess the data. The findings reveal: (i) Natural resources rents tend to increase carbon emissions consistently across different quantiles (0.1 to 0.9). The fact is confirmed by robustness checks, illustrating that increased natural resources rents lead to higher emissions. (ii) Economic, financial, and political risks affect how natural resources rents impact carbon emissions. Notably, reduced economic and financial risks lessen the propensity of natural resources rents to boost emissions at higher quantiles, while a decline in political risk decreases the exacerbating effect of natural resources rents on emissions from the 0.1 to 0.9 quantiles. (iii) This analysis uncovers threshold effects where economic, financial, and political risks act as threshold factors. Specifically, when economic and political risks are low, a rise in natural resources rents actually leads to a decline in carbon emissions. The findings underscore the importance of considering these risks in the formulation of policies aimed at reducing carbon emissions from natural resource exploitation.

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Application of ridge regression and factor analysis in design and production of alloy wheels

Zhang

2014

Journal of Applied Statistics

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Multicollinearity and financial constraint in investment decisions: a Bayesian generalized ridge regression

Cited by 6 publications

References 42 publications

Hyperspectral Inversion of Soil Organic Matter Content Based on a Combined Spectral Index Model

Hyperspectral Inversion of Soil Organic Matter Content Based on a Combined Spectral Index Model

Impact of risk factors on the link between natural resources rents and carbon emissions: Evidence from economic, financial, and political risks

Application of ridge regression and factor analysis in design and production of alloy wheels

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