Open Access
2021 Adaptive estimation in symmetric location model under log-concavity constraint
Nilanjana Laha
Author Affiliations +
Electron. J. Statist. 15(1): 2939-3014 (2021). DOI: 10.1214/21-EJS1852


We revisit the problem of estimating the center of symmetry θ of an unknown symmetric density f. Although Stone (1975), Van Eeden (1970), and Sacks (1975) constructed adaptive estimators of θ in this model, their estimators depend on external tuning parameters. In an effort to reduce the burden of tuning parameters, we impose an additional restriction of log-concavity on f. We construct truncated one-step estimators which are adaptive under the log-concavity assumption. Our simulations indicate that the untruncated version of the one step estimator, which is tuning parameter free, is also asymptotically efficient. We also study the maximum likelihood estimator (MLE) of θ in the shape-restricted model.


The author is grateful to Jon Wellner for his help.


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Nilanjana Laha. "Adaptive estimation in symmetric location model under log-concavity constraint." Electron. J. Statist. 15 (1) 2939 - 3014, 2021.


Received: 1 July 2020; Published: 2021
First available in Project Euclid: 2 June 2021

Digital Object Identifier: 10.1214/21-EJS1852

Primary: 62G99
Secondary: 62G07 , 62G20

Keywords: log-concave , one step estimator , shape constraint , symmetric location model

Vol.15 • No. 1 • 2021
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