A New Approach in Profile Analysis with High-Dimensional Data Using Scores

Abstract

In profile analysis, there exist three tests: test of parallelism, test of levels and test of flatness. In this thesis, these tests have been studied. Firstly, a classical setting, where the sample size is greater than the dimension of the parameter space, is considered.  The hypotheses have been established and likelihood ratio tests have been derived. The distributions of these test statistics have been given. In the latter stage, all tests have been derived in a high-dimensional setting, where the number of parameters exceeds the number of sample size. Such settings have become more common due to the advances in computer technologies in the last decades. In high-dimensional data analysis, several issues arise with the dimensionality and different techniques have been developed to deal with these issues.  We propose a dimension reduction method using scores that was first proposed by Läuter et al. (1996). To be able to find the specific distributions of the test statistics of profile analysis in this context, the properties of spherical distributions are utilized.

Keywords

High-dimensional data; hypothesis testing; linear scores; multivariate analysis; profile analysis; spherical distributions

Published in

Rapport (Institutionen för energi och teknik, SLU)
2020, number: 113ISBN: 978-91-576-9783-7, eISBN: 978-91-576-9784-4Publisher: Swedish University of Agricultural Sciences, Department of Energy and Technology

SLU Authors

• Cengiz, Cigdem

  • Department of Energy and Technology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Probability Theory and Statistics


Permanent link to this page (URI)

https://res.slu.se/id/publ/107196