A variance shift model for detection of outliers in the linear mixed measurement error models. Issue 24 (16th December 2016)

• Record Type: Journal Article
• Authors: Babadi, B.; Rasekh, A.; Zare, K.; Rasekhi, A. A.
• Is Part Of: Communications in statistics. Volume 45:Issue 24(2016)
• Page Start: 7350
• Page End: 7366
• Publication Date: 2016-12-16
• Subjects: Corrected likelihood -- Linear mixed measurement error models -- Score test -- Variance shift model.; Primary 62J05 -- Secondary 62J20; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2014.980517
• Languages: English
• ISSNs: 0361-0926

Abstract

ABSTRACT: In this paper, we extend a variance shift model, previously considered in the linear mixed models, to the linear mixed measurement error models using the corrected likelihood of Nakamura (1990 ). This model assumes that a single outlier arises from an observation with inflated variance. We derive the score test and the analogue of the likelihood ratio test, to assess whether the i th observation has inflated variance. A parametric bootstrap procedure is implemented to obtain empirical distributions of the test statistics. Finally, results of a simulation study and an example of real data are presented to illustrate the performance of proposed tests.