The Odd Log-Logistic Geometric Family with Applications in Regression Models with Varying Dispersion

DOI

10.2991/jsta.d.190818.003How to use a DOI?

Keywords

Geometric family; Censored data; Maximum likelihood estimation; Odd log-logistic family; Regression model; Varying dispersion

Abstract

We obtain some mathematical properties of a new generator of continuous distributions with two additional shape parameters called the odd log-logistic geometric family. We present some special models and investigate the asymptotes and shapes. The family density function can be expressed as a linear combination of exponentiated densities based on the same baseline distribution. We derive a power series for its quantile function. We provide explicit expressions for the ordinary and incomplete moments and generating function. We estimate the model parameters by maximum likelihood. We propose a useful regression model by varying the dispersion parameter to fit real data. We illustrate the potentiality of the proposed models by means of three real data sets.

Copyright

© 2019 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Maria do Carmo S. Lima
AU  - Fábio Prataviera
AU  - Edwin M. M. Ortega
AU  - Gauss M. Cordeiro
PY  - 2019
DA  - 2019/09/03
TI  - The Odd Log-Logistic Geometric Family with Applications in Regression Models with Varying Dispersion
JO  - Journal of Statistical Theory and Applications
SP  - 278
EP  - 294
VL  - 18
IS  - 3
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.d.190818.003
DO  - 10.2991/jsta.d.190818.003
ID  - Lima2019
ER  -