OneStep: Le Cam's One-step Estimation Procedure

Alexandre Brouste (), Christophe Dutang () and Darel Noutsa Mieniedou
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Alexandre Brouste: LMM - Laboratoire Manceau de Mathématiques - UM - Le Mans Université
Christophe Dutang: CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique
Darel Noutsa Mieniedou: LMM - Laboratoire Manceau de Mathématiques - UM - Le Mans Université

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Abstract: 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.

Date: 2021
Note: View the original document on HAL open archive server: https://hal.science/hal-03452455v1
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Published in The R Journal, 2021, 13 (1), pp.366. ⟨10.32614/RJ-2021-044⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03452455

DOI: 10.32614/RJ-2021-044

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