ESTIMATION OF MISSING VALUES FOR BILINEAR TIME SERIES MODELS WITH GARCH INNOVATIONS USING NONPARAMETRIC METHODS
| dc.contributor.author | ABAJA, POTI OWILI | |
| dc.date.accessioned | 2021-10-29T07:52:32Z | |
| dc.date.available | 2021-10-29T07:52:32Z | |
| dc.date.issued | 2016 | |
| dc.identifier.uri | http://ir.kabarak.ac.ke/handle/123456789/721 | |
| dc.description.abstract | A 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 data | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Kabarak University | en_US |
| dc.subject | ESTIMATION | en_US |
| dc.subject | GARCH INNOVATIONS | en_US |
| dc.subject | NONPARAMETRIC | en_US |
| dc.title | ESTIMATION OF MISSING VALUES FOR BILINEAR TIME SERIES MODELS WITH GARCH INNOVATIONS USING NONPARAMETRIC METHODS | en_US |
| dc.type | Presentation | en_US |
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