Modern variable selection techniques in the generalised linear model with application in Biostatistics

Description

In a Biostatistics environment, the datasets to be analysed are frequently high-dimensional and multicollinearity is expected due to the nature of the features. However, many traditional approaches to statistical analysis and feature selection cease to be useful in the presence of high-dimensionality and multicollinearity. Penalised regression methods have proved to be practical and attractive for dealing with these problems. In this dissertation, we propose a new penalised approach, the modified elastic-net (MEnet), for statistical analysis and feature selection using a combination of the ridge and bridge penalties. This
method is designed to deal with high-dimensional problems with highly correlated predictor variables. Furthermore, it has a closed-form solution, unlike the most frequently used penalised techniques, which makes it simple to implement on high-dimensional data. We show how this approach can be used to analyse high-dimensional data with binary responses, e.g., microarray data, and simultaneously select significant features. An extensive simulation study and analysis of a colon cancer dataset demonstrate the properties and practical aspects of the proposed method. / Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2020. / DSI-CSIR Interbursary Support (IBS) Programme / Statistics Industry HUB, Department of Statistics, University of Pretoria / Statistics / MSc / Restricted

Links & Downloads

1. http://hdl.handle.net/2263/76508
2. Millard, S 2020, Modern variable selection techniques in the generalised linear model with application in Biostatistics, MSc Mini Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/76508>

Tags

Mathematical statistics

Penalised regression

Feature selection

UCTD

Additional Fields

Identifer	oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:up/oai:repository.up.ac.za:2263/76508
Date	10 1900
Creators	Millard, Salomi
Contributors	Arashi, Mohammad, [e-mail], Maribe, G.
Publisher	University of Pretoria
Source Sets	South African National ETD Portal
Language	English
Detected Language	English
Type	Mini Dissertation
Rights	© 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.