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Quantifying heteroskedasticity via binary decomposition
conference contribution
posted on 2013-01-01, 00:00 authored by Marwa Hassan, Mohammed Hossny, Saeid Nahavandi, Douglas CreightonDouglas CreightonThis paper presents a quantifying measure for heteroskedasticity of a time series. In this research, heteroskedasticity levels are measured by decomposing the examined time series recursively into homoskedastic segments. Each segment of the examined time series is decomposed into smaller segments if it tests positively to heteroskedasticity tests. The final quantified value of the heteroskedasticity level is the number of homoskedastic segments. The proposed measure is robust and detects heteroskedasticity in small average variance datasets.
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Event
Computer Modelling and Simulation. International Conference (15th : 2013 : Cambridge, England)Pagination
112 - 116Publisher
IEEE Computer SocietyLocation
Cambridge, EnglandPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2013-04-10End date
2013-04-12ISBN-13
9780769549941Language
engPublication classification
E1 Full written paper - refereedTitle of proceedings
UKSim 2013 : Proceedings of the 15th International Conference on Computer Modelling and SimulationUsage metrics
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