Bearbmagovva

Regularized Maximum Likelihood Estimation for the Random Coefficients Model in Python

Furkejuvvon:
Publikašuvnnas:Mathematics vol. 13, no. 23 (2025), p. 3764-3793
Váldodahkki: Dunker Fabian
Eará dahkkit: Mendoza, Emil, Reale, Marco
Almmustuhtton:
MDPI AG
Fáttát:
Approximation
Maximum likelihood estimation
Python
Modules
Random variables
Algorithms
Maximum likelihood method
Liŋkkat:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Fáddágilkorat: Lasit fáddágilkoriid
Eai fáddágilkorat, Lasit vuosttaš fáddágilkora!
Abstrákta: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.
ISSN:2227-7390
DOI:10.3390/math13233764
Gáldu:Engineering Database