Non-Asymptotic Confidence Sets for Circular Means†
Institut für Mathematik, Technische Universität Ilmenau, 98684 Ilmenau, Germany
This paper is an extended version of our paper published in Proceedings of the 2nd International Conference on Geometric Science of Information, Palaiseau, France, 28–30 October 2015; Nielsen, F., Barbaresco, F., Eds.; Lecture Notes in Computer Science, Volume 9389; Springer International Publishing: Cham, Switzerland, 2015; pp. 635–642.
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Academic Editors: Frédéric Barbaresco and Frank Nielsen
Received: 15 July 2016 / Revised: 10 October 2016 / Accepted: 13 October 2016 / Published: 20 October 2016
The mean of data on the unit circle is defined as the minimizer of the average squared Euclidean distance to the data. Based on Hoeffding’s mass concentration inequalities, non-asymptotic confidence sets for circular means are constructed which are universal in the sense that they require no distributional assumptions. These are then compared with asymptotic confidence sets in simulations and for a real data set.
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MDPI and ACS Style
Hotz, T.; Kelma, F.; Wieditz, J. Non-Asymptotic Confidence Sets for Circular Means. Entropy 2016, 18, 375.
Hotz T, Kelma F, Wieditz J. Non-Asymptotic Confidence Sets for Circular Means. Entropy. 2016; 18(10):375.
Hotz, Thomas; Kelma, Florian; Wieditz, Johannes. 2016. "Non-Asymptotic Confidence Sets for Circular Means." Entropy 18, no. 10: 375.
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