TY - JOUR
T1 - Exponentiated Gull Alpha Exponential Distribution with Application to COVID-19 Data
AU - Khogeer, Hazar A.
AU - Alrumayh, Amani
AU - El-Raouf, M. M.Abd
AU - Kilai, Mutua
AU - Aldallal, Ramy
N1 - Publisher Copyright:
© 2022 Hazar A. Khogeer et al.
PY - 2022
Y1 - 2022
N2 - In this paper, the main aim is to define a statistical distribution that can be used to model COVID-19 data in Mexico and Canada. Using the method of exponentiation on the gull alpha exponential distribution introduces a new distribution with three parameters called the exponentiated gull alpha power exponential (EGAPE) distribution. The distribution has the benefit of being able to represent monotonic and nonmonotonic failure rates, both of which are often seen in dependability issues. It is possible to determine the quantile function as well as the skewness, kurtosis, and order statistics of the suggested distribution. The approach of maximum likelihood is used in order to calculate the parameters of the model, and the RMSE and average bias are utilised in order to evaluate how successful the strategy is. In conclusion, the flexibility of the new distribution is demonstrated by modeling COVID-19 data. From the practical application, we can conclude that the proposed model outperformed the competing models and therefore can be used as a better option for modeling COVID-19 and other related datasets.
AB - In this paper, the main aim is to define a statistical distribution that can be used to model COVID-19 data in Mexico and Canada. Using the method of exponentiation on the gull alpha exponential distribution introduces a new distribution with three parameters called the exponentiated gull alpha power exponential (EGAPE) distribution. The distribution has the benefit of being able to represent monotonic and nonmonotonic failure rates, both of which are often seen in dependability issues. It is possible to determine the quantile function as well as the skewness, kurtosis, and order statistics of the suggested distribution. The approach of maximum likelihood is used in order to calculate the parameters of the model, and the RMSE and average bias are utilised in order to evaluate how successful the strategy is. In conclusion, the flexibility of the new distribution is demonstrated by modeling COVID-19 data. From the practical application, we can conclude that the proposed model outperformed the competing models and therefore can be used as a better option for modeling COVID-19 and other related datasets.
UR - http://www.scopus.com/inward/record.url?scp=85132533164&partnerID=8YFLogxK
U2 - 10.1155/2022/4255079
DO - 10.1155/2022/4255079
M3 - Article
AN - SCOPUS:85132533164
SN - 2314-4629
VL - 2022
JO - Journal of Mathematics
JF - Journal of Mathematics
M1 - 4255079
ER -