Issue: 2023/Vol.33/No.4, Pages 71-95
ANALYSIS OF COVID-19 AND CANCER DATA USING NEW HALF-LOGISTIC GENERATED FAMILY OF DISTRIBUTIONS
Cite as: S. Khan, M. H. Tahir, F. Jamal. Analysis of COVID-19 and cancer data using new half-logistic generated family of distributions. Operations Research and Decisions 2023: 33(4), 71-95. DOI 10.37190/ord230405
We focus on a specific sub-model of the proposed family that we call the new half logistic- Fréchet. This sub-model stems from a new generalisation of the half-logistic distribution which we call the new half-logistic-G. The novelty of proposing this new family is that it does not include any additional parameters and instead relies on the baseline parameter. Standard statistical formulas are used to show the forms of the density and failure rate functions, ordinary and incomplete moments with generating functions, and random variate generation. The maximum likelihood estimation procedure is used to estimate the set of parameters. We conduct a simulation analysis to ensure that our calculations are converging with lower mean square error and biases. We use three real-life data sets to equate our model to well-established existing models. The proposed model outperforms the well- established four parameters beta Fréchet and exponentiated generalized Fréchet for some real-life results, with three parameters such as half-logistic Fréchet, exponentiated Fréchet, Zografos–Balakrishnan gamma Fréchet,Topp–Leonne Fréchet, and Marshall–Olkin Fréchet and two-parameter classical Fréchet distribution.
Keywords: half-logistic distribution, generalized family, maximum likelihood estimation, bio-medical data, Fréchet distribution
Received: 22 December 2022 Accepted: 17 August 2023
Published online: 22 December 2023