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Analysis of COVID-19 and cancer data using new half-logistic generated family of distributions

100%

KHAN S., TAHIR M. H., JAMAL F.

Operations Research and Decisions

2023

vol. 33

issue 4

71-95

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.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.

ANALIZA WYBRANYCH METOD ESTYMACJI PARAMETRÓW GRANICZNYCH ROZKŁADÓW STATYSTYKI MAKSIMUM

58%

Pekasiewicz D.

Metody Ilościowe w Badaniach Ekonomicznych

2015

vol. 16

issue 4

75-84

Granicznym rozkładem statystyki maksimum wyznaczonej na podstawie próby losowej jest jeden z rozkładów: Gumbela, Frécheta lub Weibulla. Gdy posiadamy informacje o klasie rozkładu analizowanej zmiennej twierdzenia graniczne określają klasę rozkładu maksimum z próby, natomiast w innym przypadku należy stosować testy statystyczne oparte na statystykach pozycyjnych rozstrzygające o przynależności dystrybuanty maksimum do obszaru przyciągania odpowiedniej dystrybuanty. Do szacowania parametrów rozkładów maksimum wykorzystać można różne metody estymacji, w szczególności metodę największej wiarygodności, metodę momentów i metody oparte na kwantylach. W pracy przedstawiono rezultaty analiz błędów średniokwadratowych estymatorów parametrów rozkładu Gumbela otrzymanych metodą momentów, kwantyli oraz kwantylową metodą najmniejszych kwadratów z uciętą liczbą kwantyli. Otrzymane wyniki pozwalają sformułować wnioski dotyczące własności rozważanych estymatorów.

The limiting distribution of maximum statistic determined on the basis of a random sample is one of the following distributions: Gumbel, Fréchet or Weibull. If we have information about the distribution class of the analyzed variable we use limit theorems about maximum distribution, otherwise we must apply appropriate statistical tests based on the order statistics. We can use different methods to estimate the parameter of maximum distribution, in particular the maximum likelihood method, the method of moments and methods based on quantiles. The paper presents the results of analysis of mean squared errors of Gumbel distribution parameters estimators obtained by the methods of moments, the quantile method and the quantile least squares method with a truncated number of quantiles. Received results allow to draw conclusions on the regarded estimators properties, specifically the efficiency of the chosen estimation methods.

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1 Metody Ilościowe w Badaniach Ekonomicznych

1 Operations Research and Decisions

1 JAMAL F.

1 KHAN S.

1 Pekasiewicz D.

1 TAHIR M. H.

1 2023

1 2015

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Analysis of COVID-19 and cancer data using new half-logistic generated family of distributions

ANALIZA WYBRANYCH METOD ESTYMACJI PARAMETRÓW GRANICZNYCH ROZKŁADÓW STATYSTYKI MAKSIMUM