The Odd Log-Logistic Geometric Family with Applications in Regression Models with Varying Dispersion
- Maria do Carmo S. Lima1, Fábio Prataviera2, Edwin M. M. Ortega2, *, Gauss M. Cordeiro11 Departamento de Estatίstica, Universidade Federal de Pernambuco, Recife, Brazil2 Departamento de Ciências Exatas, Universidade de São Paulo‚ Piracicaba, Brazil*Corresponding author. Email: firstname.lastname@example.org
- Corresponding Author
- Edwin M. M. Ortega
- https://doi.org/10.2991/jsta.d.190818.003How to use a DOI?
- Geometric family, Censored data, Maximum likelihood estimation, Odd log-logistic family, Regression model, Varying dispersion
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.
- © 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/).
Cite this article
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 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 - https://doi.org/10.2991/jsta.d.190818.003 ID - Lima2019 ER -