Some Properties and Quantile Regression for the Log-Lindley Distribution

Seng Huat Ong (), Choung Min Ng () and Subrata Chakraborty ()
Additional contact information
Seng Huat Ong: UCSI University, Institute of Actuarial Science and Data Analytics
Choung Min Ng: University of Malaya, Institute of Mathematical Sciences
Subrata Chakraborty: Dibrugarh University, Department of Statistics

A chapter in Directional and Multivariate Statistics, 2025, pp 317-337 from Springer

Abstract: Abstract There is much recent interest in distributions alternative to the classical beta distribution. In this paper, financial risk and inequality measures have been derived for the log-Lindley distribution with support on the unit interval and a robust quantile regression is also proposed. The log-Lindley distribution is parameterized in terms of its quantile function to permit the modelling of the covariate effects across the whole distribution of response, instead of restriction to the mean. The performance of the log-Lindley quantile regression is examined by Monte Carlo simulations with application to a risk management data set.

Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:sprchp:978-981-96-2004-3_16

Ordering information: This item can be ordered from
http://www.springer.com/9789819620043

DOI: 10.1007/978-981-96-2004-3_16

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().