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In this article, we present a new family of generalized distributions called the Burr III-Topp-Leone-G (BIII-TL-G). We further study in detail its structural properties including moments, probability weighted moments, distribution of order statistics, and entropy. The maximum likelihood estimation method is used to estimate the model parameters. Simulations are carried out to show the consistency and efficiency of parameter estimates and finally, real data sets are used to demonstrate the applicability of the proposed model.

Attempts have been made to define new classes of distributions that provide more flexibility for modeling data that is skewed in nature. In this work, we propose a new family of distributions namely the Marshall-Olkin Half Logistic-G (MO-HL-G) based on the generator pioneered by [Marshall and Olkin , 1997]. This new family of distributions allows for a flexible fit to real data from several fields, such as engineering, hydrology, and survival analysis. The structural 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.

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A new family of distributions called exponentiated half-logistic Odd Burr III-G (EHL-OBIII-G) is developed and studied. Mathematical and statistical properties such as the hazard function, quantile function, moments, probability weighted moments, Renyi entropy and stochastic orders are derived. The model parameters are estimated based on the maximum likelihood estimation method. The usefulness of the proposed family of distributions is demonstrated via extensive simulation studies. Finally the proposed model and its special case is applied to real data sets to illustrate its best fit and flexibility.