Nonparametric Estimation of the Random Coefficients Model in Python

2021/08/08 by Mendoza, Emil, Dunker, Fabian, Reale, Marco
#62G05 #62G07 #62P20 #65R30 #65R32 #Computation (stat.CO) #F.2.1 #FOS: Computer and information sciences #G.1 #G.1.6 #G.4 #Methodology (stat.ME)

paper · doi:10.48550/arxiv.2108.03582

Abstract

We present PyRMLE, 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.

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