Di Marzio,, M, Fensore, S, Panzera, A et al. (1 more author) (2016) A note on nonparametric estimation of circular conditional densities. Journal of Statistical Computation and Simulation, 86 (13). pp. 2573-2582. ISSN 0094-9655
Abstract
The conditional density offers the most informative summary of the relationship between explanatory and response variables. We need to estimate it in place of the simple conditional mean when its shape is not well-behaved. A motivation for estimating conditional densities, specific to the circular setting, lies in the fact that a natural alternative of it, like quantile regression, could be considered problematic because circular quantiles are not rotationally equivariant. We treat conditional density estimation as a local polynomial fitting problem as proposed by \cite{Fan et al.:1996} in the euclidean setting, and discuss a class of estimators in the cases when the conditioning variable is either circular or linear. Asymptotic properties for some members of the proposed class are derived. The effectiveness of the methods for finite sample sizes is illustrated by simulation experiments and an example using real data.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2016, Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Statistical Computation and Simulation on 18 Feb 2016, available online: http://www.tandfonline.com/10.1080/00949655.2016.1146279 |
Keywords: | Circular data, conditional densities, local polynomials, optimal smoothing, von Mises kernel; 62G07, 62G20, 65G60 |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 02 Feb 2016 11:27 |
Last Modified: | 14 Apr 2017 04:56 |
Published Version: | http://dx.doi.org/10.1080/00949655.2016.1146279 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/00949655.2016.1146279 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:94263 |