Articles
Publication Date: 2025
Journal of Statistical Theory and Practice (15598616)19(2)
In this paper, another motivation for the well-known quadratic transmuted family of distributions is pointed out and a new relation for the expected value of this family in terms of the Gini index is presented. A bug of the generalized transmuted-G family of distributions Nofal et al. (Commun Stat Theory Methods 46:4119–4136, 2016) is illustrated. In that work, the necessary conditions for the density and distribution functions are not satisfied, for some parameter values. Moreover, a new flexible family of distributions is introduced from a fresh perspective, and their key properties are studied in general forms. As an example, a new high flexible distribution is introduced and some of its important futures such as the moment generating function, moments, order statistics and the stress-strength parameter are investigated. In addition, the parameters of the proposed new distribution are estimated using the maximum likelihood method, and three real data sets are scrutinized to assess the distribution’s adequacy in providing satisfactory fits. © Grace Scientific Publishing 2025.
Publication Date: 2024
Computational Statistics (09434062)39(2)pp. 677-708
Multicollinearity among independent variables is one of the most common problems in regression models. The aftereffects of this problem, such as ill-conditioning, instability of estimators, and inflating mean squared error of ordinary least squares estimator (OLS), in the multivariate linear regression model (MLRM) are the same that of linear regression models. To combat multicollinearity, several approaches have been presented in the literature. Liu estimator (LE), as a well known estimator in this connection, has been used in linear, generalized linear, and nonlinear regression models by researchers in recent years. In this paper, for the first time, LE and jackknifed Liu estimator (JLE) are investigated in MLRM. To improve estimators in the sense of mean squared error, two known resampling methods, i.e., jackknife and bootstrap, are used. Finally, OLS, LE, and JLE are compared by a simulation study and also using a real data set, by resampling methods in MLRM. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.
