Abstract

Kernel density estimation is a widely used tool in nonparametric density estimation procedures. Choice of a kernel function and a smoothing parameter are two important issues in implementing kernel density estimation procedures. In this paper, four different kernel functions are considered in implementing an adaptive selection procedure in choosing the smoothing parameter. In simulation, a skewed bimodal density which is a mixture of two normal distributions is considered along with the standard normal and the standard exponential densities. In skewed bimodal data, parameter estimation is also explored in the context of the parameter estimation in mixtures of normal distributions. Maximum likelihood estimation procedure is implemented in parameter estimation in mixtures of normal distributions.

Advisor

Mehbahur Rahman

Committee Member

Han Wu

Committee Member

Iresha Premarathna

Committee Member

Mezbahur Rahman

Date of Degree

2019

Language

english

Document Type

Thesis

Degree

Master of Science (MS)

Department

Mathematics and Statistics

College

Science, Engineering and Technology

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.

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