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.