IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v79y2001i2p226-250.html
   My bibliography  Save this article

Double k-Class Estimators in Regression Models with Non-spherical Disturbances

Author

Listed:
  • Wan, Alan T. K.
  • Chaturvedi, Anoop

Abstract

In this paper, we consider a family of feasible generalised double k-class estimators in a linear regression model with non-spherical disturbances. We derive the large sample asymptotic distribution of the proposed family of estimators and compare its performance with the feasible generalized least squares and Stein-rule estimators using the mean squared error matrix and risk under quadratic loss criteria. A Monte-Carlo experiment investigates the finite sample behaviour of the proposed family of estimators.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:jmvana:v:79:y:2001:i:2:p:226-250
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047-259X(00)91963-8
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Carter, R. A. L. & Srivastava, V. K. & Chaturvedi, A., 1993. "Selecting a double k-class estimator for regression coefficients," Statistics & Probability Letters, Elsevier, pages 363-371.
    2. Vinod, H. D., 1981. "Improved Stein-rule estimator for regression problems," Journal of Econometrics, Elsevier, pages 125-125.
    3. Carter, R. A. L., 1981. "Improved Stein-rule estimator for regression problems," Journal of Econometrics, Elsevier, pages 113-123.
    4. Rothenberg, Thomas J, 1984. "Approximate Normality of Generalized Least Squares Estimates," Econometrica, Econometric Society, vol. 52(4), pages 811-825, July.
    5. Ullah, Aman & Ullah, Shobha, 1978. "Double k-Class Estimators of Coefficients in Linear Regression," Econometrica, Econometric Society, vol. 46(3), pages 705-722, May.
    6. Judge, G.G. & Bock, M.E., 1983. "Biased estimation," Handbook of Econometrics,in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 10, pages 599-649 Elsevier.
    7. Vinod, H. D., 1980. "Improved stein-rule estimator for regression problems," Journal of Econometrics, Elsevier, pages 143-150.
    8. Carter Hill, R. & Judge, George, 1987. "Improved prediction in the presence of multicollinearity," Journal of Econometrics, Elsevier, pages 83-100.
    9. Hill, R.Carter & Judge, George G, 1990. "Improved estimation under collinearity and squared error loss," Journal of Multivariate Analysis, Elsevier, pages 296-312.
    10. Menjoge, Shailendra S., 1984. "On double k-class estimators of coefficients in linear regression," Economics Letters, Elsevier, vol. 15(3-4), pages 295-300.
    11. Srivastava, V. K. & Chaturvedi, A., 1986. "A necessary and sufficient condition for the dominance of an improved family of estimators in linear regression models," Economics Letters, Elsevier, vol. 20(4), pages 345-349.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Xinyu & Chen, Ti & Wan, Alan T.K. & Zou, Guohua, 2009. "Robustness of Stein-type estimators under a non-scalar error covariance structure," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2376-2388, November.
    2. Chaturvedi, Anoop & Wan, Alan T. K. & Singh, Shri P., 2002. "Improved Multivariate Prediction in a General Linear Model with an Unknown Error Covariance Matrix," Journal of Multivariate Analysis, Elsevier, vol. 83(1), pages 166-182, October.
    3. 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.
    4. 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, pages 35-47.
    5. Pal, Amresh Bahadur & Dubey, Ashutosh Kumar & Chaturvedi, Anoop, 2016. "Shrinkage estimation in spatial autoregressive model," Journal of Multivariate Analysis, Elsevier, pages 362-373.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jmvana:v:79:y:2001:i:2:p:226-250. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.