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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. [ABSTRACT FROM AUTHOR]

Reducing poverty, one of the development goals, becomes a world, national and provincial priority as stated in the first of Sustainable Development Goals: No Poverty. There are three poverty indicators calculated by BPS, one of which is the poverty severity index. The poverty severity index describes the level of disparity in spending among the poor. The Central Bureau of Statistics provides poverty data at the national, provincial, and district/city levels but not at the sub-district/village levels. The need for data at a specific level, such as the sub-district, can help implement the poverty alleviation program right on target. Therefore, this study uses the SAE method with the Empirical Best Linear Unbiased Prediction (EBLUP) approach to estimate the poverty severity index at the sub-district level in Surabaya with auxiliary variables. Using Susenas data of Surabaya in 2020, the simulation with the generated data of lognormal distribution and normal distribution showed that for both generated data distributions, the EBLUP approach with the REML procedure has the smaller MSE and RRMSE than the EBLUP method with ML procedure and direct estimation method, so, REML procedure is able to produce the best estimator. The poverty severity index at kecamatan (sub-district) level in Surabaya is affected by population growth and the number of facilities and infrastructure in the village/kelurahan. Then, Kecamatan Asemrowo is the sub-district with the highest poverty severity index of 0.0207, while Kecamatan Mulyorejo has the lowest poverty severity index, which is close to zero. [ABSTRACT FROM AUTHOR]