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STATISTICAL ANALYSIS OF LONG-RANGE DEPENDENT RANDOM PROCESSES AND FIELDS

Part of: Inference from stochastic processes Parametric inference Stochastic processes

Published online by Cambridge University Press:  08 January 2020

HUDA MOHAMMED ALOMARI
HUDA MOHAMMED ALOMARI*
Affiliation:
Al-Baha University, Al-Baha, Saudi Arabia email haldrawshah@bu.edu.sa
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Abstract

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Keywords

long-memory parameterIbragimov minimum contrast estimatortapered dataGegenbauer random fieldwavelet transformsfilterestimators of parameterscyclic long memory

MSC classification

Primary: 62F12: Asymptotic properties of estimators
Secondary: 60G60: Random fields 62M30: Spatial processes
Type
Abstracts of Australasian PhD Theses
Information
Bulletin of the Australian Mathematical Society , Volume 101 , Issue 2 , April 2020 , pp. 345 - 347
Copyright
© 2020 Australian Mathematical Publishing Association Inc.

Footnotes

Thesis submitted to La Trobe University in December 2018; degree approved on 26 April 2019; principal supervisor Andriy Olenko, co-supervisor Luke Prendergast.

References

Alomari, H. M., Ayache, A., Fradon, M. and Olenko, A., ‘Estimation of seasonal long-memory parameters’, Scand. J. Stat., to appear.Google Scholar
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Bardet, J. M. and Bertrand, P. R., ‘A non-parametric estimator of the spectral density of a continuous-time Gaussian process observed at random times’, Scand. J. Stat. 37(3) (2010), 458–476.CrossRefGoogle Scholar
Espejo, R., Leonenko, N., Olenko, A. and Ruiz-Medina, M., ‘On a class of minimum contrast estimators for Gegenbauer random fields’, Test 24(4) (2015), 657–680.CrossRefGoogle Scholar
Olenko, A., ‘Limit theorems for weighted functionals of cyclical long-memory random fields’, Stoch. Anal. Appl. 31(2) (2013), 199–213.CrossRefGoogle Scholar
Whitcher, B., ‘Wavelet-based estimation for seasonal long-memory processes’, Technometrics 46(2) (2004), 225–238.CrossRefGoogle Scholar