Nonparametric Estimation of the Random Coefficients Model in Python

Autor:	Mendoza, Emil, Dunker, Fabian, Reale, Marco
Rok vydání:	2021
Předmět:	Statistics - Computation Statistics - Methodology 62G05 62G07 62P20 65R32 65R30 F.2.1 G.4 G.1.6 G.1
Druh dokumentu:	Working Paper
Popis:	We present $\textbf{PyRMLE}$, a Python module that implements Regularized Maximum Likelihood Estimation for the analysis of Random Coefficient models. $\textbf{PyRMLE}$ 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 $\textbf{NumPy}$ and $\textbf{SciPy}$ for computational efficiency. The main implementation of the algorithm is executed purely in Python code which takes advantage of Python's high-level features. Comment: 30 pages, 22 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2108.03582 Zobrazit plný text záznamu View this record from Arxiv