Abstract
We investigated the linear shrinkage and shrinkage pretest estimators in a partially linear model, when it is a priori suspected that the regression coefficient may be restricted to a subspace. Using Monte Carlo simulations, we compared their performance with those of some penalty estimators. The proposed estimators were more efficient than the penalty estimators when the number of non-significant predictors was large. The shrinkage pretest estimator is suggested for practical applications, since its performance was robust against the reliability of the restriction. The proposed estimators were also applied to a real dataset to confirm their practicality.
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Acknowledgements
The research work of S. Ejaz Ahmed was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and Thammasat University under the Bualuang ASEAN Chair Professorship grant.
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Phukongtong, S., Lisawadi, S., Ahmed, S.E. (2020). Assessing the Relative Performance of Penalty and Non-penalty Estimators in a Partially Linear Model. In: Xu, J., Duca, G., Ahmed, S., García Márquez, F., Hajiyev, A. (eds) Proceedings of the Fourteenth International Conference on Management Science and Engineering Management. ICMSEM 2020. Advances in Intelligent Systems and Computing, vol 1190. Springer, Cham. https://doi.org/10.1007/978-3-030-49829-0_36
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DOI: https://doi.org/10.1007/978-3-030-49829-0_36
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