A New Extended G Family of Continuous Distributions with Mathematical Properties, Characterizations and Regression Modeling

Abstract

We propose a new extended G family of distributions. Some of its structural properties are derived and some useful characterization results are presented. The maximum likelihood method is used to estimate the model parameters by means of graphical and numerical Monte Carlo simulation study. The flexibility of the new family illustrated by means of two real data sets. Moreover, we introduce a new log-location regression model based on the proposed family. The martingale and modified deviance residuals are defined to detect outliers and evaluate the model assumptions. The potentiality of the new regression model is illustrated by means of a real data set.

A New Flexible Three-Parameter Model: Properties, Clayton Copula, and Modeling Real Data

“…The * and * statistics are given by: * = (1 + 1/2 ) 1/(12 ) + , and: * = ( ) + , We compared the fits of the MOBE-2 distribution with some competitive models, namely: exponential (E (β)), odd Lindley exponential (OLiE), MO exponential (MOE (α, β)), moment exponential (MomE (β)), the logarithmic Burr-Hatke exponential (Log BrHE (β)), generalized MO exponential (GMOE (α, α, β)), beta exponential (BE (a, b, β)), MO-Kumaraswamy exponential (MOKwE (α, a, b, β)), Kumaraswamy exponential (KwE (a, b, β)), and Kumaraswamy MO exponential (KwMOE (α, a, b, β)). See the PDFs of the competitive moels in [21][22][23][24][25][26][27][28][29][30][31]. We considered the Cramér-Von Mises (W * ), the Anderson-Darling (A * ), and the Kolmogorov-Smirnov (KS) statistics.…”

A new three parameter Fréchet model with mathematical properties and applications

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A new extension of Lindley distribution: modified validation test, characterizations and different methods of estimation

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