Shaw and buckley

2009

Asian Journal of Probability and Statistics, 2021

Generalizing probability distributions is a very common practice in the theory of statistics. Researchers have proposed several generalized classes of distributions which are very flexible and convenient to study various statistical properties of the distribution and its ability to fit the real-life data. Several methods are available in the literature to generalize new family of distributions. The Quadratic Rank Transmutation Map (QRTM) is a tool for the construction of new families of non-Gaussian distributions and to modulate a given base distribution for modifying the moments like the skewness and kurtosis with the ability to explore its tail properties and improve the adequacy of the distribution. Recently, a new family of transmutation map, defined as Cubic Rank Transmutation (CRT) has been used by several authors to develop new distributions with application to real-life data. In this article, we have done a review work on the existing generalized rank mapped transmuted proba...

Journal of Nonlinear Sciences and Applications

Stochastics and Quality Control, 2018

In this article, a new family of distributions is introduced by using transmutation maps. The proposed family of distributions is expected to be useful in modeling real data sets. The genesis of the proposed family, including several statistical and reliability properties, is presented. Methods of estimation like maximum likelihood, least squares, weighted least squares, and maximum product spacing are discussed. Maximum likelihood estimation under censoring schemes is also considered. Further, we explore some special models of the proposed family of distributions and examined different properties of these special models. We compare three particular models of the proposed family with several existing distributions using different information criteria. It is observed that the proposed particular models perform better than different competing models. Applications of the particular models of the proposed family of distributions are finally presented to establish the applicability in re...

Journal of Xidian University, 2022

In this paper we propose a transmuted Shanker distribution (TSD) using quadratic rank transmutation map. This modification essentially introduced a second parameter in the one-parameter Shanker distribution, making it more heavy-tailed with the shape of a leptokurtic distribution. The significance of the proposed distribution was discussed as well as the statistical properties. The maximum likelihood estimation was used to estimate the parameters with a closed-form solution. Using real life data, we obtain that the TSD perform better than the Transmuted Ishita distribution, Shanker distribution as well as the Power Shanker distribution.

2000

Tukey (1960) derived via the technique of transformation of variables starting from the normal distribution a family of skewed and leptokurtic distributions. Skewness and leptokurtosis are determined by two parametersg and h. Therefore, the family was called gh-distributions. We modify Tukeys proposal such that other symmetric distributions will be taken as starting point for the transformation of variables. We speak about a family gh transformed symmetrical distributions. Especially, we condiser the Laplace distribution and the t-distribution with a fixed number of degrees. The aim ist to show, what kind of distribution take place between a leptokurtic symmetric distribution and the parameter g and . Because of numerical problems with maximum likelihood. Hoaglins (1983) technique of estimation by quantiles it used. We demonstrate how the three families of gh-transformed symmetrical distributions work fpr real financial data sets that stem from a skewed and leptokurtic distribution.

Nonlinear Analysis: Theory, Methods & Applications, 2009

We introduce a new class of continuous distributions called the generalized transmuted-G family which extends the quadratic rank trans- mutation map pioneered by Shaw and Buckley (2007). We provide six special models of the new family. Some of its mathematical properties including explicit expressions for the ordinary and incomplete moments, generating function, Rényi and Shannon entropies, order statistics and probability weighted moments are derived. The estimation of the model parameters is performed by maximum likelihood. The flexibility of the proposed family is illustrated by means of three applications to real data sets.