Improved ridge estimator in linear regression with multicollinearity, heteroscedastic errors and outliers

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Abstract

This paper introduces a new estimator, of ridge parameter k for ridge regression and then evaluated by Monte Carlo simulation. We examine the performance of the proposed estimators compared with other well-known estimators for the model with heteroscedastics and/or correlated errors, outlier observations, non-normal errors and suffer from the problem of multicollinearity. It is shown that proposed estimators have a smaller MSE than the ordinary least squared estimator (LS), Hoerl and Kennard (1970) estimator (RR), jackknifed modified ridge (JMR) estimator, and Jackknifed Ridge M-estimator (JRM).

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APA

Dorugade, A. V. (2016). Improved ridge estimator in linear regression with multicollinearity, heteroscedastic errors and outliers. Journal of Modern Applied Statistical Methods, 15(2), 362–381. https://doi.org/10.22237/jmasm/1478002860

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