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All Journal Media Statistika
Muhammad Ihsan
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
Decision Sciences, Operations Research & Management

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MULTIPLE IMPUTATION FOR ORDINARY COUNT DATA BY NORMAL DISTRIBUTION APPROXIMATION Titin Siswantining; Muhammad Ihsan; Saskya Mary Soemartojo; Devvi Sarwinda; Herley Shaori Al-Ash; Ika Marta Sari
MEDIA STATISTIKA Vol 14, No 1 (2021): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.14.1.68-78

Abstract

Missing values are a problem that is often encountered in various fields and must be addressed to obtain good statistical inference such as parameter estimation. Missing values can be found in any type of data, included count data that has Poisson distributed. One solution to overcome that problem is applying multiple imputation techniques. The multiple imputation technique for the case of count data consists of three main stages, namely the imputation, the analysis, and pooling parameter. The use of the normal distribution refers to the sampling distribution using the central limit theorem for discrete distributions. This study is also equipped with numerical simulations which aim to compare accuracy based on the resulting bias value. Based on the study, the solutions proposed to overcome the missing values in the count data yield satisfactory results. This is indicated by the size of the bias parameter estimate is small. But the bias value tends to increase with increasing percentage of observation of missing values and when the parameter values are small.
Co-Authors Devvi Sarwinda Herley Shaori Al-Ash Ika Marta Sari Saskya Mary Soemartojo Titin Siswantining