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Risk and Pitman closeness properties of feasible generalized double k-class estimators in linear regression models with non-spherical disturbances under balanced loss function

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  • Chaturvedi, Anoop
  • Shalabh

Abstract

In this article, a family of feasible generalized double k-class estimator in a linear regression model with non-spherical disturbances is considered. The performance of this estimator is judged with feasible generalized least-squares and feasible generalized Stein-rule estimators under balanced loss function using the criteria of quadratic risk and general Pitman closeness. A Monte-Carlo study investigates the finite sample properties of several estimators arising from the family of feasible double k-class estimators.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jmvana:v:90:y:2004:i:2:p:229-256
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    References listed on IDEAS

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    Cited by:

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    3. 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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