A new quantile regression model with application to human development index

Gauss M. Cordeiro, Gabriela M. Rodrigues, Fábio Prataviera and Edwin M. M. Ortega ()
Additional contact information
Gauss M. Cordeiro: Federal University of Pernambuco
Gabriela M. Rodrigues: University of São Paulo
Fábio Prataviera: University of São Paulo
Edwin M. M. Ortega: University of São Paulo

Computational Statistics, 2024, vol. 39, issue 6, No 2, 2925-2948

Abstract: Abstract A new odd log-logistic unit omega distribution is defined and studied, and some of its structural properties are obtained. A quantile regression model based on the new re-parameterized distribution is constructed, and the estimation is conducted by the maximum likelihood method. Monte Carlo simulations are used to assess the accuracy of the estimators. The flexibility, practical relevance and applicability of the proposed regression are proved by means of Human Development Index data from the cities of the state of São Paulo (Brazil).

Keywords: Log-logistic distribution; Maximum likelihood estimation; Omega distribution; Quantile regression (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-023-01413-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:39:y:2024:i:6:d:10.1007_s00180-023-01413-w

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-023-01413-w

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().