Date Available
12-19-2019
Year of Publication
2018
Degree Name
Doctor of Philosophy (PhD)
Document Type
Doctoral Dissertation
College
Arts and Sciences
Department/School/Program
Statistics
First Advisor
Dr. Derek S. Young
Abstract
Finite Mixture model has been studied for a long time, however, traditional methods assume that the variables are measured without error. Mixtures-of-regression model with measurement error imposes challenges to the statisticians, since both the mixture structure and the existence of measurement error can lead to inconsistent estimate for the regression coefficients. In order to solve the inconsistency, We propose series of methods to estimate the mixture likelihood of the mixtures-of-regressions model when there is measurement error, both in the responses and predictors. Different estimators of the parameters are derived and compared with respect to their relative efficiencies. The simulation results show that the proposed estimation methods work well and improve the estimating process.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2018.489
Recommended Citation
Fang, Xiaoqiong, "Mixtures-of-Regressions with Measurement Error" (2018). Theses and Dissertations--Statistics. 36.
https://uknowledge.uky.edu/statistics_etds/36
