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Selecting a double k-class estimator for regression coefficients

Author

Listed:
  • Carter, R. A. L.
  • Srivastava, V. K.
  • Chaturvedi, A.

Abstract

This paper considers the choice of scalars characterizing the double k-class estimators of the coefficients in a linear regression model. We demonstrate the existence of a double k-class estimator that dominates the least squares and Stein-rule estimators and we give feasible values for the characterizing scalars which nearly minimize the risk of the estimator.

Suggested Citation

  • Carter, R. A. L. & Srivastava, V. K. & Chaturvedi, A., 1993. "Selecting a double k-class estimator for regression coefficients," Statistics & Probability Letters, Elsevier, vol. 18(5), pages 363-371, December.
    • Handle: RePEc:eee:stapro:v:18:y:1993:i:5:p:363-371
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    Citations

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    Cited by:

    1. Wan, Alan T. K. & Chaturvedi, Anoop, 2001. "Double k-Class Estimators in Regression Models with Non-spherical Disturbances," Journal of Multivariate Analysis, Elsevier, vol. 79(2), pages 226-250, November.
    2. Chaturvedi, Anoop & Shalabh, 2004. "Risk and Pitman closeness properties of feasible generalized double k-class estimators in linear regression models with non-spherical disturbances under balanced loss function," Journal of Multivariate Analysis, Elsevier, vol. 90(2), pages 229-256, August.
    3. Shalabh, & Garg, G. & Heumann, C., 2012. "Performance of double k-class estimators for coefficients in linear regression models with non-spherical disturbances under asymmetric losses," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 35-47.
    4. Pal, Amresh Bahadur & Dubey, Ashutosh Kumar & Chaturvedi, Anoop, 2016. "Shrinkage estimation in spatial autoregressive model," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 362-373.

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