The OneStep package proposes principally an eponymic function that numerically computes Le Cam’s one-step estimator, which is asymptotically efficient and can be computed faster than the maximum likelihood estimator for large datasets. Monte Carlo simulations are carried out for several examples (discrete and continuous probability distributions) in order to exhibit the performance of Le Cam’s one-step estimation procedure in terms of efficiency and computational cost on observation samples of finite size.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2021-044.zip
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Brouste, et al., "The R Journal: OneStep : Le Cam's One-step Estimation Procedure", The R Journal, 2021
BibTeX citation
@article{RJ-2021-044,
author = {Brouste, Alexandre and Dutang, Christophe and Mieniedou, Darel Noutsa},
title = {The R Journal: OneStep : Le Cam's One-step Estimation Procedure},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-044},
doi = {10.32614/RJ-2021-044},
volume = {13},
issue = {1},
issn = {2073-4859},
pages = {383-394}
}