The odd Chen generator of distributions: properties and estimation methods with applications in medicine and engineering

This paper introduces a new univariate flexible generator of distributions called the odd Chen-G family. Some of its statistical properties were derived. Two special models of the proposed generator were provided. The model parameters were estimated using six estimation methods, namely, maximum likelihood estimators, least squares estimators, weighted least squares estimators, maximum product of spacings estimators, Cramer-von Mises estimators and percentile-based estimators. Further, simulations were performed to compare their performances for both small and large samples. Finally, three real datasets were used to illustrate the flexibility of the special models of the proposed family.