| dc.contributor.author | Pyeye, Sarah | |
| dc.contributor.author | Syengo, Charles K | |
| dc.contributor.author | Odongo, Leo | |
| dc.contributor.author | Orwa, George O | |
| dc.contributor.author | Otieno, Romanus Odhiambo | |
| dc.date.accessioned | 2018-11-22T17:16:54Z | |
| dc.date.accessioned | 2020-02-06T14:01:25Z | |
| dc.date.available | 2018-11-22T17:16:54Z | |
| dc.date.available | 2020-02-06T14:01:25Z | |
| dc.date.issued | 2016 | |
| dc.identifier.citation | Pyeye, S., Syengo, C. K., Odongo, L., Orwa, G. O., & Odhiambo, R. O. (2016). Longitudinal Survey, Nonmonotone, Nonresponse, Imputation, Nonparametric Regression. Open Journal of Statistics, 6(06), 1138. | en_US |
| dc.identifier.uri | http://repository.must.ac.ke/handle/123456789/980 | |
| dc.description.abstract | The study focuses on the imputation for the longitudinal survey data which often has nonignorable nonrespondents. Local linear regression is used to impute the missing values and then the estimation of the time-dependent finite populations means. The asymptotic properties (unbiasedness and consistency) of the proposed estimator are investigated. Comparisons between different parametric and nonparametric estimators are performed based on the bootstrap standard deviation, mean square error and percentage relative bias. A simulation study is carried out to determine the best performing
estimator of the time-dependent finite population means. The simulation results show that local linear regression estimator yields good properties. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Scientific Research Publishing | en_US |
| dc.subject | Longitudinal Survey, Nonmonotone, Nonresponse, Imputation, Nonparametric Regression | en_US |
| dc.title | Longitudinal Survey, Nonmonotone, Nonresponse, Imputation, Nonparametric Regression | en_US |
| dc.type | Article | en_US |
