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MULTIPLE IMPUTATION FOR ORDINARY COUNT DATA BY NORMAL DISTRIBUTION APPROXIMATION

*Titin Siswantining orcid scopus  -  Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Indonesia
Muhammad Ihsan  -  Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Indonesia
Saskya Mary Soemartojo  -  Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Indonesia
Devvi Sarwinda  -  Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Indonesia
Herley Shaori Al-Ash  -  Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Indonesia
Ika Marta Sari  -  Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Indonesia
Open Access Copyright (c) 2021 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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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.
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Keywords: count data; generelized linear model; missing value; multiple imputation; poisson regression; rubin’s rule.
Funding: Universitas Indoesia

Article Metrics:

  1. Akmam, E. F., Siswantining, T., Soemartojo, S. M., & Sarwinda, D. (2019). Multiple Imputation with Predictive Mean Matching Method for Numerical Missing Data. ICICOS 2019 - 3rd International Conference on Informatics and Computational Sciences: Accelerating Informatics and Computational Research for Smarter Society in The Era of Industry 4.0, Proceedings, February 2020. https://doi.org/10.1109/ICICoS48119.2019.8982510
  2. Anwar, T., Siswantining, T., Sarwinda, D., Soemartojo, S. M., & Bustamam, A. (2019). A Study on Missing Values Imputation using K-Harmonic Means Algorithm: Mixed datasets. AIP Conference Proceedings, 2202(December). https://doi.org/10.1063/1.5141651
  3. Aristiawati, K., Siswantining, T., Sarwinda, D., & Soemartojo, S. M. (2019). Missing values Imputation Based on Fuzzy C-Means Algorithm for Classification of Chronic Obstructive Pulmonary Disease (COPD). AIP Conference Proceedings, 2192(December). https://doi.org/10.1063/1.5139149
  4. Barbur, V. A., Montgomery, D. C., & Peck, E. A. (1994). Introduction to Linear Regression Analysis. The Statistician, 43(2), 339. https://doi.org/10.2307/2348362
  5. Christopher, S. Z., Siswantining, T., Sarwinda, D., & Bustaman, A. (2019). Missing Value Analysis of Numerical Data using Fractional Hot Deck Imputation. ICICOS 2019 - 3rd International Conference on Informatics and Computational Sciences: Accelerating Informatics and Computational Research for Smarter Society in The Era of Industry 4.0, Proceedings, February 2020. https://doi.org/10.1109/ICICoS48119.2019.8982412
  6. Conover, W. J. (2008). 857_1734 (Issue 1999)
  7. Enders, C. K. (2017). Multiple Imputation as a Flexible Tool for Missing Data Handling in Clinical Research. Behaviour Research and Therapy, 98, 4–18. https://doi.org/10.1016/j.brat.2016.11.008
  8. Falcaro, M., & Carpenter, J. R. (2017). Correcting Bias Due to Missing Stage Data in the Non-Parametric Estimation of Stage-Specific Net Survival for Colorectal Cancer Using Multiple Imputation. Cancer Epidemiology, 48, 16–21. https://doi.org/10.1016/j.canep.2017.02.005
  9. Gupta, V. K., & Grover, G. (2017). Multiple Imputation for Gamma Outcome Variable Using Generalized Linear Model. Journal of Statistical Computation and Simulation, 87(10), 1980–1988. https://doi.org/10.1080/00949655.2017.1300904
  10. Hogg Allen T Craig, R. V. (1978). Introduction to Mathematical Statistics Fourth Edition
  11. Ibrahim, J. G., Chen, M. H., Lipsitz, S. R., & Herring, A. H. (2005). Missing-Data Methods for Generalized Linear Models: A Comparative Review. In Journal of the American Statistical Association (Vol. 100, Issue 469, pp. 332–346). Taylor & Francis. https://doi.org/10.1198/016214504000001844
  12. O’Kelly, M. (2014). Multiple Imputation and Its Application. James Carpenter and Michael Kenward (2013). Chichester: John Wiley & Sons. 345 pages, ISBN: 9780470740521. Biometrical Journal, 56(2), 352–353. https://doi.org/10.1002/bimj.201300188
  13. Rubin, D. B. (Ed.). (1987). Multiple Imputation for Nonresponse in Surveys. John Wiley & Sons, Inc. https://doi.org/10.1002/9780470316696
  14. van Buuren, S. (2018). Flexible Imputation of Missing Data, Second Edition. In Flexible Imputation of Missing Data, Second Edition. https://doi.org/10.1201/9780429492259
  15. Wiegand, H. (1968). Kish, L.: Survey Sampling. John Wiley & Sons, Inc., New York, London 1965, IX + 643 S., 31 Abb., 56 Tab., Preis 83 s. Biometrische Zeitschrift, 10(1), 88–89. https://doi.org/10.1002/bimj.19680100122
  16. Wilkinson, L. (1999). Statistical Methods in Psychology Journals: Guidelines and Explanations. American Psychologist, 54(8), 594–604. https://doi.org/10.1037/0003-066X.54.8.594

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