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

Cengiz, Cigdem (2020). A New Approach in Profile Analysis with High-Dimensional Data Using Scores. Sveriges lantbruksuniv. , Rapport / Institutionen för energi och teknik, SLU, 1654-9406 ; 113
ISBN 978-91-576-9783-7
eISBN 978-91-576-9784-4
[Licentiate thesis]

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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.

Authors/Creators:Cengiz, Cigdem
Title:A New Approach in Profile Analysis with High-Dimensional Data Using Scores
Year of publishing :2020
Number:113
Number of Pages:52
Publisher:Swedish University of Agricultural Sciences, Department of Energy and Technology
ISBN for printed version:978-91-576-9783-7
ISBN for electronic version:978-91-576-9784-4
ISSN:1654-9406
Language:English
Publication Type:Licentiate thesis
Article category:Other scientific
Version:Published version
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 101 Mathematics > 10106 Probability Theory and Statistics
Keywords:High-dimensional data, hypothesis testing, linear scores, multivariate analysis, profile analysis, spherical distributions
URN:NBN:urn:nbn:se:slu:epsilon-p-107196
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-107196
ID Code:17457
Faculty:NJ - Fakulteten för naturresurser och jordbruksvetenskap
Department:(NL, NJ) > Dept. of Energy and Technology
Deposited By: SLUpub Connector
Deposited On:27 Aug 2020 12:09
Metadata Last Modified:31 Aug 2020 08:50

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