Semi-parametric mixtures of partially linear models

Abstract

This mini-dissertation considers semi-parametric finite mixtures of partially linear models with Gaussian errors and focuses on the estimation procedure for such models. The semi-parametric structure allows for flexible modelling of the expected value of the response variable. These models are used in cases where the regression structure include both parametric and non-parametric covariate structures. We demonstrate the properties of the profile likelihood expectation maximisation algorithm (PL-EM) using a simulation study. The estimation algorithm is also demonstrated on real data. Overall, the estimation procedure is adequate in estimating the parameters of the mixtures of partially linear models from the results obtained in both the simulation study and the real-world application.