Regularized Maximum Likelihood Estimation for the Random Coefficients Model in Python

Fabian Dunker, Emil Mendoza and Marco Reale ()
Additional contact information
Fabian Dunker: School of Mathematics and Statistics, University of Canterbury, Private Bag 800, Christchurch 8140, New Zealand
Emil Mendoza: School of Mathematics and Statistics, University of Canterbury, Private Bag 800, Christchurch 8140, New Zealand
Marco Reale: School of Mathematics and Statistics, University of Canterbury, Private Bag 800, Christchurch 8140, New Zealand

Mathematics, 2025, vol. 13, issue 23, 1-29

Abstract: We present PyRMLE (Python regularized maximum likelihood estimation), a Python module that implements regularized maximum likelihood estimation for the analysis of Random coefficient models. PyRMLE is simple to use and readily works with data formats that are typical to Random coefficient problems. The module makes use of Python’s scientific libraries NumPy and SciPy for computational efficiency. The main implementation of the algorithm is executed purely in Python code, which takes advantage of Python’s high-level features. The module has been applied successfully in numerical experiments and real data applications. We demonstrate an application of the package in consumer demand.

Keywords: Python; random coefficients model; penalized maximum likelihood; regularization; adaptive estimation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/23/3764/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/23/3764/ (text/html)

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:gam:jmathe:v:13:y:2025:i:23:p:3764-:d:1801434

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().