期刊
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
卷 90, 期 7, 页码 1153-1172出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00949655.2020.1718150
关键词
Inverse Gaussian Regression Model; multicollinearity; maximum likelihood estimator; Liu-type estimator; mean squared error; application of IGRM; GDP; IGRRE; IGLE; IGLTE
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.
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