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October 2020 Inference for spherical location under high concentration
Davy Paindaveine, Thomas Verdebout
Ann. Statist. 48(5): 2982-2998 (October 2020). DOI: 10.1214/19-AOS1918


Motivated by the fact that circular or spherical data are often much concentrated around a location $\pmb{\theta }$, we consider inference about $\pmb{\theta }$ under high concentration asymptotic scenarios for which the probability of any fixed spherical cap centered at $\pmb{\theta }$ converges to one as the sample size $n$ diverges to infinity. Rather than restricting to Fisher–von Mises–Langevin distributions, we consider a much broader, semiparametric, class of rotationally symmetric distributions indexed by the location parameter $\pmb{\theta }$, a scalar concentration parameter $\kappa $ and a functional nuisance $f$. We determine the class of distributions for which high concentration is obtained as $\kappa $ diverges to infinity. For such distributions, we then consider inference (point estimation, confidence zone estimation, hypothesis testing) on $\pmb{\theta }$ in asymptotic scenarios where $\kappa _{n}$ diverges to infinity at an arbitrary rate with the sample size $n$. Our asymptotic investigation reveals that, interestingly, optimal inference procedures on $\pmb{\theta }$ show consistency rates that depend on $f$. Using asymptotics “à la Le Cam,” we show that the spherical mean is, at any $f$, a parametrically superefficient estimator of ${\pmb{\theta }}$ and that the Watson and Wald tests for $\mathcal{H}_{0}:{\pmb{\theta }}={\pmb{\theta }}_{0}$ enjoy similar, nonstandard, optimality properties. We illustrate our results through simulations and treat a real data example. On a technical point of view, our asymptotic derivations require challenging expansions of rotationally symmetric functionals for large arguments of $f$.


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Davy Paindaveine. Thomas Verdebout. "Inference for spherical location under high concentration." Ann. Statist. 48 (5) 2982 - 2998, October 2020.


Received: 1 January 2019; Revised: 1 June 2019; Published: October 2020
First available in Project Euclid: 19 September 2020

MathSciNet: MR4152631
Digital Object Identifier: 10.1214/19-AOS1918

Primary: 62E20, 62F30
Secondary: 62F05, 62F12

Rights: Copyright © 2020 Institute of Mathematical Statistics


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Vol.48 • No. 5 • October 2020
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