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

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Listed:
  • Babak Babadi
  • Abdolrahman Rasekh
  • Ali Akbar Rasekhi
  • Karim Zare
  • Mohammad Reza Zadkarami

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.

Suggested Citation

Handle: RePEc:wly:jnlaaa:v:2014:y:2014:i:1:n:396875
DOI: 10.1155/2014/396875
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