A variance shift model for detection of outliers in the linear mixed measurement error models

B. Babadi, A. Rasekh, K. Zare and A. A. Rasekhi

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 24, 7350-7366

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 ith 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.

Date: 2016
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DOI: 10.1080/03610926.2014.980517

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