Project description

SF4wD

four-component stochastic frontier model with determinants

This package was developed to complement four-component stochastic frontier that consider

determinants in mean and variance parameters of inefficiency distributions

by Ruei-Chi Lee.

SF4wD: main.py - set method and model to run simulation or real data
HMC: Hamilton Monte Carlo designed for determinants parameters.
DA: Data augmentation for the model
TK: Two-parametrization method originally proposed by Tsiona and Kunmbhaker (2014) for four-component model without determinants.
PMCMC: Particle MCMC for the model (perferred approach) - speed up by GPU parallel computation

Ruei-Chi Lee is the main author and contributor.

Bug reports, feature requests, questions, rants, etc are welcome, preferably on the github page.

These details have not been verified by PyPI

View statistics for this project via Libraries.io, or by using our public dataset on Google BigQuery

0.0.2

Jul 30, 2021

This version

0.0.1

Jul 23, 2021

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Uploaded Jul 23, 2021 Source

Uploaded Jul 23, 2021 Python 3

Hashes for 4SFwD-0.0.1.tar.gz
Algorithm	Hash digest
SHA256	`8bbc2b4f2cf7fb1705ec4855f728338a4300a88dfe297704eb3f5a25914ab7bf`
MD5	`78705421e59ac97c5e0ac58416828c82`
BLAKE2b-256	`5da0c62cd2013891c9271422e035ef84c4b868bf8bc7f8d7247b1d00db01e0ea`

Hashes for 4SFwD-0.0.1-py3-none-any.whl
Algorithm	Hash digest
SHA256	`54b7ca2c00ef00cfdb4a860b753b4ade0d185148f36c77a04f2471d69a625222`
MD5	`12859425a88c338780b292b33788661f`
BLAKE2b-256	`c106bac0de24039820cdb5a57daeda2f57ccdbb48f2720162eb32fc5417e65f1`