Published Paper Details:

The Exponentiated non-negative Rayleigh Poisson distribution is introduced in this research work by combining A new generalization of Rayleigh distribution; characteristics and applications with The Exponentiated G Poisson model. This study's primary goal is to use the power transformation technique to increase the Exponentiated G. Poisson distribution's flexibility. The new proposed model's probability density function, survival function, and hazard function are shown graphically. We examine this new distribution's characteristics, paying particular attention to its moments, skewness, kurtosis, mode, and quantile function. The probability-weighted moments, order statistics, R'enyi, entropies, and the residual life function have all been covered. We computed the log-likelihood, Bayesian, Akaike, and corrected Akaike information criteria and Hennan-Quinn information criteria of the following distributions: the newly proposed Exponentiated Rayleigh Poisson distribution, the Compound Rayleigh distribution, the Exponentiated Chen distribution, the Exponentiated Exponential distribution, the Exponentiated Inverted Weibull distribution, and the Generalized Rayleigh distribution. We discovered that the newly proposed model has smaller values than the others. For model validation, we have examined the TTT plot, Q-Q plot, Kolmogorov Smirnov test, and P-P plot of the suggested distribution. We contrasted the proposed model's estimated distributed function CDF and empirical distribution CDF with those of five other models. For illustrative purposes, an actual dataset is examined. By using various methods and resources, the new family's significance and adaptability are demonstrated. The paper is concluded with a final conclusion.