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has been cited by the following article:

• TITLE: Longitudinal Survey, Nonmonotone, Nonresponse, Imputation, Nonparametric Regression

  AUTHORS: Sarah Pyeye, Charles K. Syengo, Leo Odongo, George O. Orwa, Romanus O. Odhiambo

  KEYWORDS: Longitudinal Survey, Nonmonotone, Nonresponse, Imputation, Nonparametric Regression

  JOURNAL NAME: Open Journal of Statistics, Vol.6 No.6, December 27, 2016

  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.