Date of Award

7-7-2020

Publication Type

Master Thesis

Degree Name

M.Sc.

Department

Mathematics and Statistics

First Advisor

Mohamed Belalia

Keywords

Nonparametric estimation

Rights

info:eu-repo/semantics/openAccess

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Abstract

In this thesis, we propose a new nonparametric approach based on Bernstein polynomials to estimate the conditional density function. The proposed estimators have desired properties at the boundaries and can outperform the kernel and local linear estimators in terms of Integrated Mean Square Error for an appropriate choice of the polynomials' order. The idea is to construct a two-stage conditional probability density function estimator based on Bernstein polynomials. Specifically, the Nadaraya-Watson (NW) and local linear (LL) conditional distribution function estimators were smoothed using Bernstein polynomials in the first stage. Secondly, the proposed estimators are obtained by differentiating the smoothed Bernstein NW and LL estimators.

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