Monitoring Mortality Caused by COVID-19 Using Gamma-Distributed Variables Based on Generalized Multiple Dependent State Sampling | Zendy

Muhammad Aslam | Zendy; G. Srinivasa Rao | Zendy; Muhammad Saleem | Zendy; Rehan Ahmad Khan Sherwani | Zendy; ChiHyuck Jun | Zendy

Open Access

Monitoring Mortality Caused by COVID-19 Using Gamma-Distributed Variables Based on Generalized Multiple Dependent State Sampling

Author(s) -

Muhammad Aslam,

G. Srinivasa Rao,

Muhammad Saleem,

Rehan Ahmad Khan Sherwani,

ChiHyuck Jun

Publication year - 2021

Publication title -

computational and mathematical methods in medicine

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.462

H-Index - 48

eISSN - 1748-6718

pISSN - 1748-670X

DOI - 10.1155/2021/6634887

Subject(s) - control chart , sampling (signal processing) , shewhart individuals control chart , statistics , gamma distribution , parametric statistics , chart , computer science , statistical process control , x bar chart , sampling design , mathematics , ewma chart , population , demography , filter (signal processing) , process (computing) , sociology , computer vision , operating system

More recently in statistical quality control studies, researchers are paying more attention to quality characteristics having nonnormal distributions. In the present article, a generalized multiple dependent state (GMDS) sampling control chart is proposed based on the transformation of gamma quality characteristics into a normal distribution. The parameters for the proposed control charts are obtained using in-control average run length (ARL) at specified shape parametric values for different specified average run lengths. The out-of-control ARL of the proposed gamma control chart using GMDS sampling is explored using simulation for various shift size changes in scale parameters to study the performance of the control chart. The proposed gamma control chart performs better than the existing multiple dependent state sampling (MDS) based on gamma distribution and traditional Shewhart control charts in terms of average run lengths. A case study with real-life data from ICU intake to death caused by COVID-19 has been incorporated for the realistic handling of the proposed control chart design.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research