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SELECTING THE “TRUE” REGRESSION MODEL: A NEW RANKING METHOD
Keywords:
multiple linear regression, information criteria, bootstrapping, ranking methodDOI:
https://doi.org/10.17654/0972361723025Abstract
Statistical regression modeling is widely used in many areas of scientific research. An optimum regression model, containing most suitable predictors their combinations, is a desire of every researcher. This paper deals with selecting a “true” regression model. The paper is based upon a new method of identifying a “true” regression model for an available data set. The method is based upon bootstrapping and averaging some available criteria and then, assuming multivariate normality of averages, using the method of Zhu [1] to select a true regression model. The results are supported by an extensive simulation study. We found that our proposed method is best in selecting the “true” regression model in simulation study as well as in the real data application. It is, therefore, suggested that the proposed method can be used to pick a suitable regression model.
Received: February 13, 2023; Accepted: March 14, 2023; Published: April 12, 2023
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