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A new Liu-type estimator for the Inverse Gaussian Regression Model
Department of Statistics, University of Sargodha, Sargodha, Pakistan.
Department of Statistics, University of Sargodha, Sargodha, Pakistan.
Jönköping University, Jönköping International Business School, JIBS, Statistics.
2020 (English)In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, p. 1-20Article in journal (Refereed) Epub ahead of print
Abstract [en]

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.

Place, publisher, year, edition, pages
Taylor & Francis , 2020. p. 1-20
Keywords [en]
Inverse Gaussian Regression Model, multicollinearity, maximum likelihood estimator, Liu-type estimator, mean squared error, application of IGRM, GDP, IGRRE, IGLE, IGLTE
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:hj:diva-47710DOI: 10.1080/00949655.2020.1718150ISI: 000509027000001Scopus ID: 2-s2.0-85078462163Local ID: ;IHHÖvrigtISOAI: oai:DiVA.org:hj-47710DiVA, id: diva2:1390821
Available from: 2020-02-03 Created: 2020-02-03 Last updated: 2020-02-17

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Qasim, Muhammad

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