In this study, we introduce a new generalized family of distributions called the Exponentiated Half Logistic-Harris-G (EHL-Harris-G) distribution, which extends the Harris-G distribution. The motivation for introducing this generalized family of distributions lies in its ability to overcome the limitations of previous families, enhance flexibility, improve tail behavior, provide better statistical properties and find applications in several fields. Several statistical properties, including hazard rate function, quantile function, moments, moments of residual life, distribution of the order statistics and Rényi entropy are discussed. Risk measures, such as value at risk, tail value at risk, tail variance and tail variance premium, are also derived and studied. To estimate the parameters of the EHL-Harris-G family of distributions, the following six different estimation approaches are used: maximum likelihood (MLE), least-squares (LS), weighted least-squares (WLS), maximum product spacing (MPS), Cramér–von Mises (CVM), and Anderson–Darling (AD). The Monte Carlo simulation results for EHL-Harris-Weibull (EHL-Harris-W) show that the MLE method allows us to obtain better estimates, followed by WLS and then AD. Finally, we show that the EHL-Harris-W distribution is superior to some other equi-parameter non-nested models in the literature, by fitting it to two real-life data sets from different disciplines.

The aim of this paper is to introduce a generalized LASSO regression model that is derived using a generalized Laplace (GL) distribution. Five different GL distributions are obtained through the T -R{Y } framework with quantile functions of standard uniform, Weibull, log-logistic, logistic, and extreme value distributions. The properties, including quantile function, mode, and Shannon entropy of these GL distributions are derived. A particular case of GL distributions called the beta-Laplace distribution is explored. Some additional components to the constraint in the ordinary LASSO regression model are obtained through the Bayesian interpretation of LASSO with beta-Laplace priors. The geometric interpretations of these additional components are presented. The effects of the parameters from beta-Laplace distribution in the generalized LASSO regression model are also discussed. Two real data sets are analyzed to illustrate the flexibility and usefulness of the generalized LASSO regression model in the process of variable selection with better prediction performance. Consequently, this research study demonstrates that more flexible statistical distributions can be used to enhance LASSO in terms of flexibility in variable selection and shrinkage with better prediction.

This study introduces a novel statistical family known as the Ristić-Balakrishnan-Heavy-Tailed-Type II Topp-Leone-G (RB-HT-TIITL-G). The research explores key statistical properties, including hazard rate function, quantile function, moments, stochastic ordering, and order statistics. Furthermore, essential risk measures such as value at risk, tail variance, and tail variance premium are derived. Several parameter estimation methods, including maximum likelihood, least squares, weighted least squares, maximum product spacing, Cramér-von Mises, and Anderson-Darling estimators, are applied. Monte Carlo simulations evaluate the consistency of these estimation techniques. Lastly, the model’s effectiveness is validated using real-world data, demonstrating its suitability for modeling diverse data sets.

We propose a new extension of the Weibull-exponentiated half logistic-G (WEHL-G) distribution, called the Marshall-Olkin-Weibull-exponentiated half logistic-G (MO-WEHL-G) family of distributions. The properties of this family of distributions including quantile function, distribution of the order statistics, hazard rate function, Rényi entropy and moments of residual life are presented. To estimate the parameters of the MO-WEHL-G family of distributions, six different estimation approaches are used, namely, maximum likelihood, Anderson-Darling, Ordinary Least Squares, Weighted Least Squares, Cramér-von Mises and Maximum Product of Spacing. The consistency properties of the six estimation methods were assessed using Monte Carlo simulations for a special case of the MO-WEHL-G family of distributions. The flexibility and importance of the proposed model was assessed using numerous goodness-of-fit statistics on three different data sets.