On the size corrected tests in improved estimation

Hoque, Zahirul and Billah, Baki and Khan, Shahjahan (2005) On the size corrected tests in improved estimation. Calcutta Statistical Association Bulletin, 57 (227-22). pp. 143-160. ISSN 0008-0683


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In this paper we propose shrinkage preliminary
test estimator (SPTE) of the coefficient vector in the multiple
linear regression model based on the size corrected Wald (W),
likelihood ratio (LR) and Lagrangian multiplier (LM) tests.
The correction factors used are those obtained from degrees of
freedom corrections to the estimate of the error variance and
those obtained from the second-order Edgeworth approximations to
the exact distributions of the test statistics. The bias and
weighted mean squared error (WMSE) functions of the estimators are
derived. With respect to WMSE, the relative efficiencies of the
SPTEs relative to the maximum likelihood estimator are calculated.
This study shows that the amount of conflict can be substantial
when the three tests are based on the same asymptotic chi-square
critical value. The conflict among the SPTEs is due to the
asymptotic tests not having the correct significance level. The
Edgeworth size corrected W, LR and LM tests reduce the
conflict remarkably.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information (displayed to public): Full citation: Hoque, Z., Billah, B. and Khan, S. (2005). On the Size Corrected Tests in Improved Estimation. Calcutta Statistical Association Bulletin, Vol 57 (127-128), 143-160.
Depositing User: Professor Shahjahan Khan
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 11 Oct 2007 00:37
Last Modified: 02 Jul 2013 22:36
Uncontrolled Keywords: Shrinkage preliminary test estimator; Wald, likelihood ratio and Lagrangian multiplier tests; bias and quadratic bias; %weighted mean squared error; relative efficiency; and conflicts.
Fields of Research (FoR): 01 Mathematical Sciences > 0104 Statistics > 010401 Applied Statistics
URI: http://eprints.usq.edu.au/id/eprint/1238

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