A generalised censored least squares and smoothing spline estimators of regression function Online publication date: Fri, 07-Jan-2022
by Ilhem Laroussi
International Journal of Mathematics in Operational Research (IJMOR), Vol. 20, No. 4, 2021
Abstract: In this work, new regression function estimators based on the combination of two approaches are proposed. The first one is the general censorship which gathers the left, right, twice and the double censorship in the same context. The second approach is inspired from non-parametric estimation methods. The least squares and smoothing spline techniques are chosen in this paper. Moreover, the almost sure convergence of the proposed estimators to the optimal value is established. The main results extend and improve the obtained results in Boukeloua and Messaci (2016) and Kebabi et al. (2011) allowing for a more flexible methodology to deal with different censorship models including the four types of censoring (left, right, twice and double). Simulation study illustrates the performance of these estimators. We only considered a model subject to twice censorship because the other cases have already been studied in previous works.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Mathematics in Operational Research (IJMOR):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook