ESTIMATION OF MISSING VALUES FOR BILINEAR TIME SERIES MODELS WITH GARCH INNOVATIONS USING NONPARAMETRIC METHODS

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dc.contributor.authorABAJA, POTI OWILI
dc.date.accessioned2021-10-29T07:52:32Z
dc.date.available2021-10-29T07:52:32Z
dc.date.issued2016
dc.description.abstractA time series is defined as data recorded sequentially over a specified period. Since the data are records taken overtime, missing observations in time series are very common. They may occur as a result of lost records, deletion of outliers, calender effects and defective measuring instruments Imputation is a necessary part of preprocessing of time series dataen_US
dc.identifier.urihttp://ir.kabarak.ac.ke/handle/123456789/721
dc.language.isoenen_US
dc.publisherKabarak Universityen_US
dc.subjectESTIMATIONen_US
dc.subjectGARCH INNOVATIONSen_US
dc.subjectNONPARAMETRICen_US
dc.titleESTIMATION OF MISSING VALUES FOR BILINEAR TIME SERIES MODELS WITH GARCH INNOVATIONS USING NONPARAMETRIC METHODSen_US
dc.typePresentationen_US

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6th Annual Conference Kabarak University 2016