19 research outputs found
Type II Exponentiated Half-Logistic-Topp-Leone-G Power Series Class of Distributions with Applications: Type II Exponentiated Half-Logistic-Topp-Leone-G Power Series
No full textThis paper aims to develop a new class of distributions, namely, type II exponentiated half-logistic Topp-Leone power series (TIIEHL-TL-GPS) class of distributions. Some important properties including moments, quantiles, moment generating function, entropy and maximum likelihood estimates are derived. A simulation is conducted study to evaluate the consistency of the maximum likelihood estimates. We also present three real data examples to illustrate the usefulness of the new class of distributions. Results shows that the proposed model performs better than nested and several non-nested models on selected data set
The odd Weibull-Topp-Leone-G power series family of distributions: model, properties, and applications
No full textA new generalization of the odd Weibull-Topp-Leone-G family of distributions called the odd Weibull-Topp-Leone-G power series family of distributions is developed. Statistical properties of the new distribution were derived. We also derive the maximum likelihood estimates of the proposed model. Some special cases for the new family of distributions were also considered. We conducted a simulation study to evaluate the consistency of the maximum likelihood estimates. Two real data examples were also considered to demonstrate the usefulness of the newly proposed family of distributions
The New Topp-Leone-Heavy-Tailed Type II Exponentiated Half Logistic-G Family of Distributions: Properties, Actuarial Measures, with Applications to Censored Data
No full textThe Topp-Leone heavy-tailed type II exponentiated half logistic-G (TL-HT-TIIEHL-G) is the newly proposed familyof distributions (FoDs) introduced in this research. The study thoroughly investigates the statistical properties ofthis FoDs, as well as its relevance in actuarial risk assessment. The estimation of the unknown model parameters is done using the method of maximum likelihood estimation, and the consistency of these estimates is assessed through the implementation of Monte Carlo simulations. Additionally, numerical simulations are conducted to analyze the risk measures associated with the TL-HT-TIIEHL-G FoDs. The Topp-Leone heavy-tailed type II exponentiated half logistic-Weibull (TL-HT-TIIEHL-W) distribution, a particular case of the TL-HT-TIIEHL-G FoDs is compared with other contending distributions including heavy-tailed distributions to evaluate its performance. The model’s capacity, adaptability, and practicality are convincingly showcased through its application to real data
The odd power generalized Weibull-G power series class of distributions: properties and applications
No full textWe develop a new class of distributions, namely, the odd power generalizedWeibull-G power series (OPGW-GPS) class of distributions. We present some special classes of the proposed distribution. Structural properties, have also been derived. We conducted a simulation study to evaluate the consistency of the maximum likelihood estimates. Moreover, two real data examples on selected data sets, to illustrate the usefulness of the new class of distributions. The proposed model outperforms several non-nested models on selected data sets
Exponentiated Half Logistic-Power Generalized Weibull-G Family of Distributions: Model, Properties and Applications
No full textA new family of distributions is developed by generalizing the power generalized Weibull distribution. The new distribution is referred to as the Exponentiated Half Logistic-Power Generalized-G (EHL-PGW-G) distribution. The distribution contains many nested sub-families. Also, the new family can be traced to the exponentiated-G distribution. We provide four special cases for the proposed family of distributions We applied the exponentiated half logistic-power generalized-log logistic distribution to one reliability data set and one lifetime data set. The exponentiated half logistic-power generalized-log logistic distribution performs better than a variety of selected equi-parameter competing non-nested models
La media familia logística exponencial de Topp-Leone-Gompertz de distribución con aplicaciones
No full text This paper introduces and investigates a new family of distributions called the Topp-Leone-Gompertz-exponentiated half logistic-G (TL-Gom-EHL-G) distribution. Some mathematical and statistical properties of this family of distributions are derived. To estimate and evaluate the model parameters, the maximum likelihood estimation technique is used, and the consistency of maximum likelihood estimators is examined using Monte Carlo simulation. Applications to three real data sets from different areas were used to demonstrates the usefulness and versatility of the TL-Gom-EHL-G family of distributions. Este artículo presenta e investiga una nueva familia de distribuciones denominada distribución Topp-Leone-Gompertz-exponenciada media logística-G (TL-Gom-EHL-G). Se derivan algunas propiedades matemáticas y estadísticas de esta familia de distribuciones. Para estimar y evaluar los parámetros del modelo se utiliza la técnica de estimación de máxima verosimilitud y se examina la consistencia de los estimadores de máxima verosimilitud mediante simulación de Monte Carlo. Se utilizaron aplicaciones a tres conjuntos de datos reales de diferentes áreas para demostrar la utilidad y versatilidad de la familia de distribuciones TL-Gom-EHL-G
Marshall-Olkin-Odd Power Generalized Weibull-G Family of Distributions with Applications of COVID-19 Data
No full textAttempts have been made to define new families of distributions that provide more flexibility for modeling data that is skewed in nature. In this work, we propose a new family of distributions called Marshall-Olkin-odd power generalized Weibull (MO-OPGW-G) distribution based on the generator pioneered by Marshall and Olkin [20]. This new family of distributions allows for a flexible fit to real data from several fields, such as engineering, hydrology, and survival analysis. The mathematical and statistical properties of these distributions are studied and its model parameters are obtained through the maximum likelihood method. We finally demonstrate the effectiveness of these models via simulation experiments and applications to COVID-19 daily death data sets
Type I heavy-tailed family of generalized Burr III distributions: properties, actuarial measures, regression and applications
No full textThis study introduces a new family of distributions (FoD) called type I heavy-tailed odd Burr III-G (TI-HT-OBIII-G) distribution. Several statistical properties of the family are derived along with actuarial risk measures. The maximum likelihood estimation (MLE) approach is adopted in the parameter estimation process. The estimates are evaluated centered on mean square errors and average bias via the Monte Carlo simulation framework. A regression model is formulated and the residual analysis is investigated. Members of the new FoD are applied to heavy-tailed data sets and compared to some well-known competing heavytailed distributions. The practicality, flexibility and importance of the new distribution in modeling is empirically proven using three data sets
The Exponentiated Half Logistic-Topp-Leone-G Power Series Class of Distributions: Model, Properties and Applications
No full textWe develop a new class of distributions, namely, the exponentiated half logistic-Topp-Leone-G power series (EHL-TL-GPS) class of distributions. We present some special classes in the proposed distribution. Structural properties were also derived including moments, entropy and maximum likelihood estimates. We conducted a simulation study to evaluate the consistency of the maximum likelihood estimates. We also present two real data examples to illustrate the applicability of the new class of distributions. The proposed model performs better than several non-nested models on selected data sets
A new Lindley-Burr XII power series distribution: model, properties and applications
No full textA new generalized class of distributions called the Lindley-Burr XII Power Series (LBXIIPS) distribution is proposed and explored. This new class of distributions contain some special cases such as Lindley-Burr XII Poisson (LBXIIP), Lindley-Burr XII Logarithmic (LBXIIL), Lindley-Burr XII Binomial (LBXIIB) and their sub-models among others. Some structural properties of the new distribution including moments, probability weighted moments, distribution of the order statistics and entropy are derived. Maximum likelihood estimation technique is used to estimate the model parameters. A simulation study to examine the bias and mean square error of the maximum likelihood estimators is presented and finally, an application to a real data set in order to illustrate the usefulness of the new distribution is given
