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An Investigation into Properties of Jackknifed and Bootstrapped Liu-type Estimator

Title:

An Investigation into Properties of Jackknifed and Bootstrapped Liu-type Estimator

Chaubey, Yogendra P. ORCID: https://orcid.org/0000-0002-0234-1429, Khurana, Mansi and Chandra, Shalini (2018) An Investigation into Properties of Jackknifed and Bootstrapped Liu-type Estimator. Far East Journal of Mathematical Sciences, 106 (1). pp. 159-170. ISSN 0972-0863

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Official URL: https://dx.doi.org/10.17654/MS106010159

Abstract

In 2003, Liu proposed a new estimator dealing with the problem of multicollinearity in linear regression model pointing out a drawback of ridge estimator used in this context. This new estimator, called Liu-type estimator was demonstrated to have lesser mean squared error than ridge estimator and ordinary least squares estimator, however, it may carry a large amount of bias. In the present paper, we propose different estimators in order to reduce the bias of Liu-type estimator, one using the Jackknife technique and other using the technique proposed in Kadiyala \cite{kad1984}. We also investigate the Bootstrap method of bias correction on the Liu-type estimator as well. The bias and mean squared error of these estimators have been compared using a simulation study as well as a numerical example.
LSTA-2016-0059

Divisions:Concordia University > Faculty of Arts and Science > Mathematics and Statistics
Item Type:Article
Refereed:Yes
Authors:Chaubey, Yogendra P. and Khurana, Mansi and Chandra, Shalini
Journal or Publication:Far East Journal of Mathematical Sciences
Date:3 May 2018
Funders:
  • Natural Sciences and Engineering Research Council of Canada (NSERC)
Digital Object Identifier (DOI):10.17654/MS106010159
Keywords:Multicollinearity, Liu-type estimator, Ridge estimator, Jackknife technique, bootstrap technique. This research is partly based on doctoral Bootstrap technique.
ID Code:980861
Deposited By: YOGENDRA CHAUBEY
Deposited On:04 Feb 2016 15:52
Last Modified:17 Jun 2020 15:43
Related URLs:

References:

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