• Communications for Statistical Applications and Methods
• /
• v.26 no.3
• /
• pp.325-336
• /
• 2019

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.

• Proceedings of the Korean Statistical Society Conference
• /
• 2001.11a
• /
• pp.143-148
• /
• 2001

A dataset having missing observations is often completed by using imputed values. In this paper the performances and accuracy of complete case methods and four imputation procedures are evaluated when missing values exist only on the response variables in the Weibull regression model. Our simulation results show that compared to other imputation procedures, in particular, hotdeck and Weibull regression imputation procedure can be well used to compensate for missing data. In addition an illustrative real data is given.

• Communications for Statistical Applications and Methods
• /
• v.8 no.3
• /
• pp.815-822
• /
• 2001

The aim of this study is to estimate the seroconversion date of the human immunodeficiency virus(HIV) infection for the HIV infected patients in Korea. Data are collected from two cohorts. The first cohort is a group of "seroprevalent" patients who were seropositive and AIDS-free at entry. The other is a group of "seroincident" patients who were initially seronegative but later converted to HIV antibody-positive. The seroconversion dates of the seroincident cohort are available while those of the seroprevalent cohort are not. Estimation of seroconversion date is important because it can be used to calculate the incubation period of AIDS which is defined as the elapsed time between the HIV infection and the development of AIDS. In this paper, a Weibull regression model Is fitted for the seroincident cohort using information about the elapsed time since seroconversion and the CD4$^{+}$ cell count.The seroconversion dates for the seroprevalent cohort are imputed on the basis of the marker of maturity of HIV infection percent of CD4$^{+}$cell count.unt.