Fahrezal Zubedi, Khairil Anwar Notodiputro, Bagus Sartono
Longitudinal data consists of repeated observations made from time to time on each individual. The correlation between observations within the same unit in longitudinal data makes the Generalized Linear Mixed Model (GLMM) an appropriate method for the analysis of longitudinal data. GLMM will not have a good estimation if the data contains missing values. To solve this problem, the imputation method is used. This paper discusses two imputation methods, namely mean and median row. The missing value is filled with the average or median value of all the values in the subject. It is known that poverty cases in districts/cities in Eastern Indonesia contain the missing value. Thus, poverty cases in these areas can be used as case studies of the data used. The purpose of the study is to form a GLMM model longitudinal with the Poisson response variable on poverty data in Eastern Indonesia to obtain factors that positively and negatively affect poverty in eastern Indonesia and obtain the right method of median row and mean row in the imputation methods to handle missing values based on the AIC value of the resulting estimate using GLMM. A mean row method is recommended to impute the missing values in Longitudinal data of poverty cases in Eastern Indonesia’s districts/cities. © 2024 American Institute of Physics Inc.. All rights reserved.
Department of Statistics, Faculty of Mathematics and Natural Sciences, IPB University, Bogor, Indonesia; Statistics Study Program, Faculty of Mathematics and Natural Sciences, Gorontalo State University, Gorontalo, Indonesia
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