Applications of resampling methods in multivariate Liu estimator

Shima Pirmohammadi () and Hamid Bidram ()
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Shima Pirmohammadi: University of Isfahan
Hamid Bidram: University of Isfahan

Computational Statistics, 2024, vol. 39, issue 2, No 12, 677-708

Abstract: Abstract Multicollinearity among independent variables is one of the most common problems in regression models. The aftereffects of this problem, such as ill-conditioning, instability of estimators, and inflating mean squared error of ordinary least squares estimator (OLS), in the multivariate linear regression model (MLRM) are the same that of linear regression models. To combat multicollinearity, several approaches have been presented in the literature. Liu estimator (LE), as a well known estimator in this connection, has been used in linear, generalized linear, and nonlinear regression models by researchers in recent years. In this paper, for the first time, LE and jackknifed Liu estimator (JLE) are investigated in MLRM. To improve estimators in the sense of mean squared error, two known resampling methods, i.e., jackknife and bootstrap, are used. Finally, OLS, LE, and JLE are compared by a simulation study and also using a real data set, by resampling methods in MLRM.

Keywords: Bootstrap; Ill-conditioning; Jackknife; LE; Mean squared error; Multicollinearity (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00180-022-01316-2

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