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A new Liu-type esti...
A new Liu-type estimator for the Inverse Gaussian Regression Model
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- Akram, Muhammad Nauman (författare)
- Department of Statistics, University of Sargodha, Sargodha, Pakistan
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- Amin, Muhammad (författare)
- Department of Statistics, University of Sargodha, Sargodha, Pakistan
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- Qasim, Muhammad (författare)
- Jönköping University,IHH, Statistik
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(creator_code:org_t)
- 2020-01-23
- 2020
- Engelska.
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Ingår i: Journal of Statistical Computation and Simulation. - : Taylor & Francis. - 0094-9655 .- 1563-5163. ; 90:7, s. 1153-1172
- Relaterad länk:
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https://urn.kb.se/re...
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visa fler...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- The Inverse Gaussian Regression Model (IGRM) is used when the response variable is positively skewed and follows the inverse Gaussian distribution. In this article, we propose a Liu-type estimator to combat multicollinearity in the IGRM. The variance of the Maximum Likelihood Estimator (MLE) is overstated due to the presence of severe multicollinearity. Moreover, some estimation methods are suggested to estimate the optimal value of the shrinkage parameter. The performance of the proposed estimator is compared with the MLE and some other existing estimators in the sense of mean squared error through Monte Carlo simulation and different real-life applications. Under certain conditions, it is concluded that the proposed estimator is superior to the MLE, ridge, and Liu estimator.
Ämnesord
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
Nyckelord
- Inverse Gaussian Regression Model
- multicollinearity
- maximum likelihood estimator
- Liu-type estimator
- mean squared error
- application of IGRM
- GDP
- IGRRE
- IGLE
- IGLTE
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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