In this article, four new families named as Weibull extended exponentiated-X (WEE-X), Lomax extended exponentiated-X (LEE-X), Logistic extended exponentiated-X (LGCEE-X), and Burr III extended exponentiated-X (BIIIEE-X) with their quantile functions are proposed. The expressions for distribution function and density function of BIIIEE-X family are written in terms of linear combinations of the exponentiated densities based to parent model. New models, i.e., Weibul extended exponentiated Weibull (WEEW), Lomax extended exponentiated Weibull (LEEW), Logistic extended exponentiated Weibull (LGCEEW), and Burr III extended exponentiated-Weibull (BIIIEEW) distributions are derived, were plotted for functions of probability density and hazard rate at different levels of parameters. Some mathematical properties of the BIIIEEW model are disclosed. The maximum likelihood method for the BIIIEEW model are described. Numerical applications of the BIIIEEW model to disease of cancer datasets are provided.

The study of hydrological characteristics has a vital role in designing, planning, and managing water resources. The selection of appropriate probability distributions and methods of estimations are basic elements in hydrology analyses. In this article, a new family named the ‘exponentiated power alpha index generalized’ (EPAIG)-G is proposed to develop several new distributions. Using this proposed family, we developed a new model, called the EPAIG-exponential (EPAIG-E). A few structural properties of the EPAIG-G were obtained. The EPAIG-E parameters were estimated through the method of maximum likelihood (MML). The study of the Monte Carlo simulation (MCS) was produced for the EPAIG-E. The model performance is illustrated using real data.

The repeated measurement design (RMD) is a cost-effective research design commonly used in various fields. RMDs have several advantages; however, the carryover effect is a fundamental issue. Carryover effects typically serve as the primary source of bias in the evaluation of treatment efficacy. To reduce this bias, minimal circular balanced RMDs (MCBRMDs) are utilized. Rule I of the cyclic shift method produces MCBRMDs for only the odd v (number of treatments to be compared). Rule II produces these designs for both v odd and v even. This article contributes to the literature by providing a systematic comparison of two cyclic shift rules for constructing MCBRMDs for odd v. The study provides useful guidance to experimenters in choosing effective designs under practical experimental restrictions by comparing these designs using efficiency of carryover effects and separability.