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Abstract and Applied Analysis
Volume 2014 (2014), Article ID 396875, 9 pages
Research Article

A Variance Shift Model for Detection of Outliers in the Linear Measurement Error Model

1Department of Statistics, Shahid Chamran University, Ahvaz, Iran
2Department of Biostatistics, Tarbiat Modares University, Tehran, Iran
3Department of Statistics, Islamic Azad University, Science and Research Branch, Fars, Iran

Received 27 May 2014; Accepted 5 August 2014; Published 14 September 2014

Academic Editor: Allan Peterson

Copyright © 2014 Babak Babadi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We present a variance shift model for a linear measurement error model using the corrected likelihood of Nakamura (1990). This model assumes that a single outlier arises from an observation with inflated variance. The corrected likelihood ratio and the score test statistics are proposed to determine whether the th observation has an inflated variance. A parametric bootstrap procedure is used to obtain empirical distributions of the test statistics and a simulation study has been used to show the performance of proposed tests. Finally, a real data example is given for illustration.