A two-parameter estimator in linear measurement error model

ABSTRACT

This article is concerned with the parameter estimation in linear measurement error model when there is ill-conditioned data. To deal with the multicollinearity problem, a new two-parameter estimator is proposed. The asymptotic properties of the new estimator are considered using the mean squared error matrix. Finally, a Monte Carlo simulation is presented to show the performances of the estimators in terms of simulated mean squared error criteria. According to the results, the new estimator can be suggested as an alternative to the other existing estimators in the presence of ill-conditioned data.

KEYWORDS:

• Ridge regression estimator
• Liu estimator
• two-parameter estimator
• mean squared error matrix

MATHEMATICS SUBJECT CLASSIFICATIONS:

• 62G08
• 62J05
• 62J07

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was sponsored by the Natural Science Foundation of Chongqing [grant number cstc2020jcyj-msxmX0028] and the Scientific Technological Research Program of Chongqing Municipal Education Commission [grant number KJQN202001321].