Parametric Modeling Approach to Covid-19 Pandemic Data ()
Affiliation(s)
1Department of Statistics, University of Lagos, Akoka, Nigeria.
2Department of Statistics, Ekiti State University, Ado-Ekiti, Ekiti State, Nigeria.
3Department of Mathematics, Yaba College of Technology, Lagos, Nigeria.
ABSTRACT
The
problem of skewness is common among clinical trials and survival data, which has
been the research focus derivation and proposition of different flexible
distributions. Thus, a new distribution called Extended Rayleigh Lomax
distribution is constructed from Rayleigh Lomax distribution to capture the
excessiveness of some survival data. We derive the new distribution by using
beta logit function proposed by Jones (2004). Some statistical properties of
the distribution such as density, cumulative density, reliability rate, hazard
rate, reverse hazard rate, moment generating and likelihood functions;
skewness, kurtosis and coefficient of variation are obtained. We also performed
the expected estimation of model parameters by maximum likelihood; goodness of
fit and model selection criteria, including Anderson Darling, CramerVon Misses, Kolmogorov Smirnov (KS), Akaike
Information, Bayesian Information, and
Consistent Akaike Information Criterion is employed
to select the better distribution from those models considered in the work. The
results from the statistics criteria show that the intended distribution performs well and has a good representation of the States in Nigeria’s Covid-19
death cases data than other competing models.
Share and Cite:
Badmus, N. , Faweya, O. and Ige, S. (2023) Parametric Modeling Approach to Covid-19 Pandemic Data.
Open Journal of Statistics,
13, 61-73. doi:
10.4236/ojs.2023.131005.
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