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Longitudinal Survey, Nonmonotone, Nonresponse, Imputation, Nonparametric Regression
Pan African University Institute for Basic Sciences, Technology and Innovation, Nairobi, Kenya
Pan African University Institute for Basic Sciences, Technology and Innovation, Nairobi, Kenya
Department of Statistics and Actuarial Science, Kenyatta University, Nairobi, Kenya
Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
- 1 Pan African University Institute for Basic Sciences, Technology and Innovation, Nairobi, Kenya
- 2 Pan African University Institute for Basic Sciences, Technology and Innovation, Nairobi, Kenya
- 3 Department of Statistics and Actuarial Science, Kenyatta University, Nairobi, Kenya
- 4 Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
- 5 Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Open Journal of Statistics·Volume 06 (2016)·Pages 1138–1154·Published 14 November 2016·DOI10.4236/ojs.2016.66092
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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.
KeywordsLongitudinal SurveyNonmonotoneNonresponseImputationNonparametric Regression
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