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

Babak Babadi, Abdolrahman Rasekh, Ali Akbar Rasekhi, Karim Zare and Mohammad Reza Zadkarami

Abstract and Applied Analysis, 2014, vol. 2014, issue 1

Abstract: 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 ith 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.

Date: 2014
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https://doi.org/10.1155/2014/396875

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jnlaaa:v:2014:y:2014:i:1:n:396875

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