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Properties of shrinkage estimators in linear regression when disturbances are not normal

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  • Ullah, A.
  • Srivastava, V. K.
  • Chandra, R.

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  • Ullah, A. & Srivastava, V. K. & Chandra, R., 1983. "Properties of shrinkage estimators in linear regression when disturbances are not normal," Journal of Econometrics, Elsevier, vol. 21(3), pages 389-402, April.
    • Handle: RePEc:eee:econom:v:21:y:1983:i:3:p:389-402
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    Cited by:

    1. Sanjay Verma & R. Singh, 2003. "A modified generalized mixed regression estimator when disturbances are nonnormal," Statistical Papers, Springer, vol. 44(2), pages 233-248, April.
    2. Shalabh,, 2013. "A revisit to efficient forecasting in linear regression models," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 161-170.
    3. Yong Bao & Aman Ullah, 2009. "Expectation of Quadratic Forms in Normal and Nonnormal Variables with Econometric Applications," Working Papers 200907, University of California at Riverside, Department of Economics, revised Jun 2009.
    4. Feng Xu & Zekai He, 2020. "Testing slope homogeneity in panel data models with a multifactor error structure," Statistical Papers, Springer, vol. 61(1), pages 201-224, February.
    5. Sanjay Verma & R. Karan Singh, 2002. "Estimation in restricted regression model with multivariate t distributed error," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1-2), pages 67-82.
    6. Achille VERNIZZI & Rino GOLLER & Paolo SAIS, 1995. "On the Use of Shrinkage Estimators in Filtering Extraneous Information," Departmental Working Papers 1995-15, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano, revised 12 May 2016.
    7. Čížek, Pavel, 2004. "(Non) Linear Regression Modeling," Papers 2004,11, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).

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