Length-of-Stay of Hospitalized COVID-19 Patients Using Bootstrap Quantile Regression

The purpose of this study is to identify the best model of the length-of-stay (LoS) hospitalized for patients with COVID-19 in West Sumatra, Indonesia. The LoS data is skewed to the right or violates linear model assumptions;thus, a quantile approach is employed. The asymptotic variance of quantile regression is estimated by constructing the confidence interval for the parameter of interest. This study will compare the result based on five different methods in resampling bootstrap. This study proves that wild bootstrap quantile tends to produce the shortest confidence interval. This study found that Diagnosis and Final outcome are statistically significant to give impact to the LoS hospitalized COVID-19 patients. [ABSTRACT FROM AUTHOR] Copyright of IAENG International Journal of Applied Mathematics is the property of Newswood Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)