Developing flexible classes of distributions to account for both skewness and bimodality

06/30/2021
by   Jamil Ownuk, et al.
0

We develop two novel approaches for constructing skewed and bimodal flexible distributions that can effectively generalize classical symmetric distributions. We illustrate the application of introduced techniques by extending normal, student-t, and Laplace distributions. We also study the properties of the newly constructed distributions. The method of maximum likelihood is proposed for estimating the model parameters. Furthermore, the application of new distributions is represented using real-life data.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

08/22/2018

Bivariate Discrete Inverse Weibull Distribution

In this paper, we propose a new class of bivariate distributions, called...
10/29/2019

Sine-skewed toroidal distributions and their application in protein bioinformatics

In the bioinformatics field, there has been a growing interest in modell...
04/26/2018

On Bivariate Discrete Weibull Distribution

Recently, Lee and Cha (2015, `On two generalized classes of discrete biv...
07/20/2021

Log-symmetric models with cure fraction with application to leprosy reactions data

In this paper, we propose a log-symmetric survival model with cure fract...
03/22/2015

Asymmetric Distributions from Constrained Mixtures

This paper introduces constrained mixtures for continuous distributions,...
03/16/2021

A Note on Over- and Under-Representation Among Populations with Normally-Distributed Traits

Among several subpopulations of a given species with a normally-distribu...
09/23/2019

Robust Inference for Skewed data in Health Sciences

Health data are often not symmetric to be adequately modeled through the...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.