A New Transmuted Generalized Lomax Distribution: Properties and Applications to COVID‐19 Data

Abstract

A new five-parameter transmuted generalization of the Lomax distribution (TGL) is introduced in this study which is more flexible than current distributions and has become the latest distribution theory trend. Transmuted generalization of Lomax distribution is the name given to the new model. This model includes some previously unknown distributions. The proposed distribution's structural features, closed forms for an rth moment and incomplete moments, quantile, and Rényi entropy, among other things, are deduced. Maximum likelihood estimate based on complete and Type-II censored data is used to derive the new distribution's parameter estimators. The percentile bootstrap and bootstrap-t confidence intervals for unknown parameters are introduced. Monte Carlo simulation research is discussed in order to estimate the characteristics of the proposed distribution using point and interval estimation. Other competitive models are compared to a novel TGL. The utility of the new model is demonstrated using two COVID-19 real-world data sets from France and the United Kingdom.

Weighted power Maxwell distribution: Statistical inference and COVID-19 applications

“…In this section, we focus on three real data analyses which are relevant to Covid-19. The first data is taken from [ 29 ] and represents the United Kingdom transformed Covid-19 data and spans 82 days, from May 1, 2021 to July 16, 2021. For the first dataset, the transformation was performed by [ 29 ] according to the following procedure: where ND is daily new deaths, CC is daily cumulative cases, CD is daily cumulative deaths and x i is the transformed data.…”

“…The first data is taken from [ 29 ] and represents the United Kingdom transformed Covid-19 data and spans 82 days, from May 1, 2021 to July 16, 2021. For the first dataset, the transformation was performed by [ 29 ] according to the following procedure: where ND is daily new deaths, CC is daily cumulative cases, CD is daily cumulative deaths and x i is the transformed data. The first data is given as folllows: 0.0023, 0.0023, 0.0023, 0.0046, 0.0065, 0.0067, 0.0069, 0.0069, 0.0091, 0.0093, 0.0093, 0.0093, 0.0111, 0.0115, 0.0116, 0.0116, 0.0119, 0.0133, 0.0136, 0.0138, 0.0138, 0.0159, 0.0161, 0.0162, 0.0162, 0.0162, 0.0163, 0.0180, 0.0187, 0.0202, 0.0207, 0.0208, 0.0225, 0.0230, 0.0230, 0.0239, 0.0245, 0.0251, 0.0255, 0.0255, 0.0271, 0.0275, 0.0295, 0.0297, 0.0300, 0.0302, 0.0312, 0.0314, 0.0326, 0.0346, 0.0349, 0.0350, 0.0355, 0.0379, 0.0384, 0.0394, 0.0394, 0.0412, 0.0419, 0.0425, 0.0461, 0.0464, 0.0468, 0.0471, 0.0495, 0.0501, 0.0521, 0.0571, 0.0588, 0.0597, 0.0628, 0.0679, 0.0685, 0.0715, 0.0766, 0.0780, 0.0942, 0.0960, 0.0988, 0.1223, 0.1343, and 0.1781.…”

“…In this section, we focus on three real data analyses which are relevant to Covid-19. The first data is taken from [29] and represents the United Kingdom transformed Covid-19 data and…”

A novel extended model with versatile shaped failure rate: Statistical inference with Covid -19 applications

“…The detail of this dataset can be seen in Abu El Azm et al. [45] . The second real data collection presented COVID-19 data from Italy, which spans 111 days from April 1 to July 20, 2020 and is available at https://covid19.who.int/.…”

“… [46] investigated it. See [47] , [48] for other examples of COVID-19 data applications. To assess the pertinent parameters of models, the MLE method has been utilized.…”