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The compounding method for finding bivariate noncentral distributions

Dissertation (MSc)--University of Pretoria, 2014.

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Other Authors: Bekker, Andriette, 1958-
Format: Thesis
Language:English
Published: University of Pretoria 2021
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access_status_str Open Access
author2 Bekker, Andriette, 1958-
author_browse Bekker, Andriette, 1958-
author_facet Bekker, Andriette, 1958-
collection Thesis
dc_rights_str_mv © 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.
description Dissertation (MSc)--University of Pretoria, 2014.
format Thesis
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institution University of Pretoria (South Africa)
language English
last_indexed 2026-06-10T12:37:06.816Z
license_str Other — see source repository
provenance_str_mv Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository
publishDate 2021
publishDateRange 2021
publishDateSort 2021
publisher University of Pretoria
publisherStr University of Pretoria
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source_str UPSpace — University of Pretoria Institutional Repository
spelling oai:repository.up.ac.za:2263/79704 The compounding method for finding bivariate noncentral distributions Bekker, Andriette, 1958- andriette.bekker@up.ac.za Arashi, Mohammad Ferreira, Johannes Theodorus Bivariate Composites Compounding Noncentral Poisson UCTD Dissertation (MSc)--University of Pretoria, 2014. The univariate and bivariate central chi-square- and F distributions have received a decent amount of attention in the literature during the past few decades; the noncentral counterparts of these distributions have been much less present. This study enriches the existing literature by proposing bivariate noncentral chi-square and F distributions via the employment of the compounding method with Poisson probabilities. This method has been used to a limited extent in the field of distribution theory to obtain univariate noncentral distributions; this study extends some results in literature to the corresponding bivariate setting. The process which is followed to obtain such bivariate noncentral distributions is systematically described and motivated. Some distributions of composites (univariate functions of the dependent components of the bivariate distributions) are derived and studied, in particular the product, ratio, and proportion. The benefit of introducing these bivariate noncentral distributions and their respective composites is demonstrated by graphical representations of their probability density functions. Furthermore, an example of possible application is given and discussed to illustrate the versatility of the proposed models. Statistics MSc Unrestricted 2021-04-30T12:21:58Z 2021-04-30T12:21:58Z 2014 2014-04 Dissertation Ferreira, JT 2014, The compounding method for finding bivariate noncentral distributions, MSc Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/79704> M14/9/157 http://hdl.handle.net/2263/79704 en © 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. application/pdf University of Pretoria
spellingShingle Bivariate
Composites
Compounding
Noncentral
Poisson
UCTD
The compounding method for finding bivariate noncentral distributions
title The compounding method for finding bivariate noncentral distributions
title_full The compounding method for finding bivariate noncentral distributions
title_fullStr The compounding method for finding bivariate noncentral distributions
title_full_unstemmed The compounding method for finding bivariate noncentral distributions
title_short The compounding method for finding bivariate noncentral distributions
title_sort compounding method for finding bivariate noncentral distributions
topic Bivariate
Composites
Compounding
Noncentral
Poisson
UCTD
url http://hdl.handle.net/2263/79704