Abstract
Inference under uncertain prior information has been common for several decades in statistical and related literature. In the context of a regression model, we introduce the basic notion of full model, submodel, pretest, and shrinkage estimation strategies. We briefly discuss some penalty estimators and compare it with nonpenalty estimators.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahmed, S. E. (1997). Asymptotic shrinkage estimation: The regression case. Applied Statistical Science, II, 113–139.
Bancroft, T. A. (1944). On biases in estimation due to the use of preliminary tests of significances. Annals of Mathematical Statistics, 15, 190–204.
James, W., & Stein, C. (1961). Estimation with quadratic loss. Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability (pp. 361–379).
Stein, C. (1956). The admissibility of hotelling’s \({T}^2\)-test. Mathematical Statistics, 27, 616–623.
Zeggini, E., Weedon, M. N., Lindgren, C. M., Frayling, T. M., Elliott, K. S., Lango, H., et al. (2007). Replication of genome-wide association signals in uk samples reveals risk loci for type 2 diabetes. Science, 316(5829), 1336–1341.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2014 The Author(s)
About this chapter
Cite this chapter
Ahmed, S.E. (2014). Estimation Strategies. In: Penalty, Shrinkage and Pretest Strategies. SpringerBriefs in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-03149-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-03149-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03148-4
Online ISBN: 978-3-319-03149-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)