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New ridge estimator...
New ridge estimators in the inverse Gaussian regression : Monte Carlo simulation and application to chemical data
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- Amin, Muhammad (författare)
- Department of Statistics, University of Sargodha, Sargodha, Punjab, Pakistan
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- Qasim, Muhammad (författare)
- Jönköping University,IHH, Statistik
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- Afzal, Saima (författare)
- Department of Statistics, Bahauddin Zakariya University, Multan, Punjab, Pakistan
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- Naveed, Khalid (författare)
- Department of Statistics, Bahauddin Zakariya University, Multan, Punjab, Pakistan
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(creator_code:org_t)
- 2020-08-04
- 2022
- Engelska.
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Ingår i: Communications in statistics. Simulation and computation. - : Taylor & Francis. - 0361-0918 .- 1532-4141. ; 51:10, s. 6170-6187
- Relaterad länk:
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https://doi.org/10.1...
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https://www.tandfonl...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- In numerous application areas, when the response variable is continuous, positively skewed, and well fitted to the inverse Gaussian distribution, the inverse Gaussian regression model (IGRM) is an effective approach in such scenarios. The problem of multicollinearity is very common in several application areas like chemometrics, biology, finance, and so forth. The effects of multicollinearity can be reduced using the ridge estimator. This research proposes new ridge estimators to address the issue of multicollinearity in the IGRM. The performance of the new estimators is compared with the maximum likelihood estimator and some other existing estimators. The mean square error is used as a performance evaluation criterion. A Monte Carlo simulation study is conducted to assess the performance of the new ridge estimators based on the minimum mean square error criterion. The Monte Carlo simulation results show that the performance of the proposed estimators is better than the available methods. The comparison of proposed ridge estimators is also evaluated using two real chemometrics applications. The results of Monte Carlo simulation and real applications confirmed the superiority of the proposed ridge estimators to other competitor methods.
Ämnesord
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
Nyckelord
- Inverse Gaussian ridge regression
- Mean square error
- Monte Carlo simulation
- Multicollinearity
- ridge estimators
- Gaussian distribution
- Inverse problems
- Maximum likelihood estimation
- Regression analysis
- Effective approaches
- Inverse Gaussian distribution
- Maximum likelihood estimator
- Minimum mean square error criterion
- Performance evaluation criteria
- Positively skewed
- Real applications
- Monte Carlo methods
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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