Shrinkage estimation applied to a semi-nonparametric regression model
Author:
Affiliation:
1. Department of Statistical and Actuarial Sciences , The University of Western Ontario , London , Ontario , Canada
2. Mathematics and Statistics , Brock University , St. Catahrines , Ontario , L2S3A1, Canada
Abstract
Funder
Natural Sciences and Engineering Research Council of Canada
Publisher
Walter de Gruyter GmbH
Subject
Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability
Link
https://www.degruyter.com/document/doi/10.1515/ijb-2018-0109/pdf
Reference18 articles.
1. Ma, W, Feng, Y, Chen, K, Ying, Z. Functional and parametric estimation in a semi- and nonparametric model with application to mass-spectrometry data. Int J Biostat 2015;11:285–303. https://doi.org/10.1515/ijb-2014-0066.
2. Fan, J, Gijbels, I. Local polynomial modelling and its applications. London: Chapman & Hall; 1996.
3. Robinson, PM. Root-N consistent semiparametric regression. Econometrica 1988;55:931–54. https://doi.org/10.2307/1912705.
4. Stone, CJ. Optimal global rates of convergence for nonparametric regression. Ann Math Stat 1982;10:1040–53. https://doi.org/10.1214/aos/1176345969.
5. Ruppert, D, Wand, M, Carroll, R. Semiparametric regression. New York: Cambridge University Press; 2003.
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