Principal components estimator for measurement error models


Siray G.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, vol.90, no.6, pp.1022-1038, 2020 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 90 Issue: 6
  • Publication Date: 2020
  • Doi Number: 10.1080/00949655.2020.1713133
  • Journal Name: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
  • Journal Indexes: Science Citation Index Expanded, Scopus, Academic Search Premier, Aerospace Database, Business Source Elite, Business Source Premier, CAB Abstracts, Communication Abstracts, Metadex, Veterinary Science Database, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.1022-1038
  • Keywords: Measurement error, multicollinearity, reliability matrix, principal components estimator, restricted estimator

Abstract

In this paper, we carry out the principal components regression approach to the measurement error models. We introduce the principal components estimator and then the restricted principal components estimator by combining the approaches principal components regression estimator and restricted least squares estimator for the measurement error models, when the reliability matrix known and unknown, separately. We investigate the asymptotic properties and matrix mean squared error performances of the new estimators. Also, we conduct a Monte Carlo simulation study and a numerical example to investigate the performances of the proposed estimators by the scalar mean squared error criterion.