Performance of Ridge Regression Approach in Linear Measurement Error Models with Replicated Data

Authors

  • Abdol Rasoul Ziaei Department of Statistics, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
  • Karim Zare Department of Statistics, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
  • Ayoub Sheikhi Department of Statistics, Faculty of Mathematics , Shahid Bahonar University, Kerman, Iran

DOI:

https://doi.org/10.30495/jme.v0i0.1581

Keywords:

Measurement error model, Ridge regression, Multicollinearity, Corrected log-likelihood

Abstract

It is well known that bias in parameter estimates arises
when there are measurement errors in the covariates of regression mod-
els. One solution for decreasing such biases is the use of prior informa-
tion concerning the measurement error, which is often called replication
data. In this paper, we present a ridge estimator in replicated measure-
ment error (RMER) to overcome the multicollinearity problem in such
models. The performance of RMER against some other estimators is
investigated. Large sample properties of our estimator are derived and
compared with other estimators using a simulation study as well as a
real data set.

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Published

2021-01-05

Issue

Vol. 15

Section

Vol. 15, No. 4, (2021)

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