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Application of discrete Weibull regression model with multiple imputation
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Type
Academic journal
Author
Hanna Yoo (Busan University of Foreign Studies)
Journal
한국통계학회 CSAM(Communications for Statistical Applications and Methods) Vol.26 No.3 KCI Accredited Journals SCOPUS
Published
2019.5
Pages
325 - 336 (12page)

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Application of discrete Weibull regression model with multiple imputation
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In this article we extend the discrete Weibull regression model in the presence of missing data. Discrete Weibull regression models can be adapted to various type of dispersion data however, it is not widely used. Recently Yoo (Journal of the Korean Data and Information Science Society, 30, 11–22, 2019) adapted the discrete Weibull regression model using single imputation. We extend their studies by using multiple imputation also with several various settings and compare the results. The purpose of this study is to address the merit of using multiple imputation in the presence of missing data in discrete count data. We analyzed the seventh Korean National Health and Nutrition Examination Survey (KNHANES VII), from 2016 to assess the factors influencing the variable, 1 month hospital stay, and we compared the results using discrete Weibull regression model with those of Poisson, negative Binomial and zero-inflated Poisson regression models, which are widely used in count data analyses. The results showed that the discrete Weibull regression model using multiple imputation provided the best fit. We also performed simulation studies to show the accuracy of the discrete Weibull regression using multiple imputation given both under- and over-dispersed distribution, as well as varying missing rates and sample size. Sensitivity analysis showed the influence of mis-specification and the robustness of the discrete Weibull model. Using imputation with discrete Weibull regression to analyze discrete data will increase explanatory power and is widely applicable to various types of dispersion data with a unified model.
#discrete count data #discrete Weibull regression model #missing data #multiple imputation #KNHANES

Contents

Abstract
1. Introduction
2. Discrete Weibull regression model with multiple imputation
3. Application to the seventh Korean National Health and Nutrition Examination Survey data (KNHANES VII, 2016)
4. Simulation studies
5. Discussion
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