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

Author

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

  • Babak Babadi & Abdolrahman Rasekh & Ali Akbar Rasekhi & Karim Zare & Mohammad Reza Zadkarami, 2014. "A Variance Shift Model for Detection of Outliers in the Linear Measurement Error Model," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnlaaa:v:2014:y:2014:i:1:n:396875
    DOI: 10.1155/2014/396875
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    References listed on IDEAS

    as
    1. Xu-Ping Zhong & Bo-Cheng Wei & Wing-Kam Fung, 2000. "Influence Analysis for Linear Measurement Error Models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 52(2), pages 367-379, June.
    2. Patricia Giménez & María Patat, 2014. "Local influence for functional comparative calibration models with replicated data," Statistical Papers, Springer, vol. 55(2), pages 431-454, May.
    3. Karim Zare & Abdolrahman Rasekh & Ali Rasekhi, 2012. "Estimation of variance components in linear mixed measurement error models," Statistical Papers, Springer, vol. 53(4), pages 849-863, November.
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