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Parameter estimation based on empirical transforms

Besbeas, Panagiotis (1999) Parameter estimation based on empirical transforms. Doctor of Philosophy (PhD) thesis, University of Kent. (doi:10.22024/UniKent/01.02.86100) (KAR id:86100)

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In this thesis, we provide a unified treatment of the topic of parameter estimation using integral transforms, such as the characteristic function and moment generating function. This topic encompasses a wealth of methods, which typically vary from each other in relation to the type of weight function and choice of integral transform that is being employed.

We show that the integrated squared error method dominates alternative transform methods, particularly in terms of robustness. We present a convenient and

flexible approach to dealing with the difficulty here surrounding the necessary weight function, and illustrate the success of this approach on the mixture of two normal distributions. Furthermore, we show that the integrated squared error method also outperforms the maximum likelihood method for this distribution,

particularly with samples with outliers or a small number of observations.

Item Type: Thesis (Doctor of Philosophy (PhD))
DOI/Identification number: 10.22024/UniKent/01.02.86100
Additional information: This thesis has been digitised by EThOS, the British Library digitisation service, for purposes of preservation and dissemination. It was uploaded to KAR on 09 February 2021 in order to hold its content and record within University of Kent systems. It is available Open Access using a Creative Commons Attribution, Non-commercial, No Derivatives ( licence so that the thesis and its author, can benefit from opportunities for increased readership and citation. This was done in line with University of Kent policies ( If you feel that your rights are compromised by open access to this thesis, or if you would like more information about its availability, please contact us at and we will seriously consider your claim under the terms of our Take-Down Policy (
Uncontrolled keywords: Statistics
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
SWORD Depositor: SWORD Copy
Depositing User: SWORD Copy
Date Deposited: 29 Oct 2019 16:28 UTC
Last Modified: 14 Feb 2022 12:13 UTC
Resource URI: (The current URI for this page, for reference purposes)
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