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Augmented Lagrange for constrained optimizations in empirical likelihood estimations

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posted on 28.03.2022, 12:36 authored by Andrew Locke
Empirical Likelihood is a useful tool for parameter estimation and inference as it does not require knowledge about where thedata comes from. A large strength is its applicability with different methods, it can be extended in many ways including regression or adding constraints using estimating equations. The positivity constraint of pi has often been overlooked or ignored but this means existing methodsmay experience difficulties for some problems. This thesis looks at enforcing this constraint by applying the Karush–Kuhn–Tucker conditions together with a multiplicative iterative optimization method of updating parameters which ensures movement towards the constrained maximum. For other equality constraints, we apply Augmented Lagrange to the Empirical Likelihood maximisation. We demonstrate our method using simulation examples in linear regression and estimating equations on raw moments.

History

Table of Contents

1. Introduction -- 2. Empirical Likelihood methods -- 3. Method -- 4. Simulation -- 5. Conclusion.

Notes

Empirical thesis. Bibliography: pages 53-54

Awarding Institution

Macquarie University

Degree Type

Thesis MRes

Degree

MRes, Macquarie University, Faculty of Science and Engineering, Department of Statistics

Department, Centre or School

Department of Statistics

Year of Award

2015

Principal Supervisor

Jun Ma

Rights

Copyright Andrew Locke 2015. Copyright disclaimer: http://www.copyright.mq.edu.au

Language

English

Extent

1 online resource (viii, 54 pages) graphs

Former Identifiers

mq:44908 http://hdl.handle.net/1959.14/1073145