Efficiency of Nonparametric Estimators for Missing Observations of Bilinear Time Series with Gaussian Innovation
| dc.contributor.author | OWILI ABAJA, POTI | |
| dc.contributor.author | Nassiuma, Dankt | |
| dc.contributor.author | Orawo, Dr Luke | |
| dc.date.accessioned | 2021-10-29T07:58:09Z | |
| dc.date.available | 2021-10-29T07:58:09Z | |
| dc.date.issued | 2016 | |
| dc.identifier.uri | http://ir.kabarak.ac.ke/handle/123456789/722 | |
| 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 Being unable to account for missing data has several limitations: A severe miss-representation of the phenomenon under study | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Kabarak University | en_US |
| dc.subject | Nonparametric Estimators | en_US |
| dc.subject | Bilinear Time Series | en_US |
| dc.title | Efficiency of Nonparametric Estimators for Missing Observations of Bilinear Time Series with Gaussian Innovation | en_US |
| dc.type | Presentation | en_US |
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