Cheng, Y. and Ray, S. (2014) Multivariate modality inference using Gaussian kernel. Open Journal of Statistics, 4(5), pp. 419-434. (doi: 10.4236/ojs.2014.45041)
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Abstract
The number of modes (also known as modality) of a kernel density estimator (KDE) draws lots of interests and is important in practice. In this paper, we develop an inference framework on the modality of a KDE under multivariate setting using Gaussian kernel. We applied the modal clustering method proposed by [1] for mode hunting. A test statistic and its asymptotic distribution are derived to assess the significance of each mode. The inference procedure is applied on both simulated and real data sets.
Item Type: | Articles |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Ray, Professor Surajit |
Authors: | Cheng, Y., and Ray, S. |
Subjects: | H Social Sciences > HA Statistics |
College/School: | College of Science and Engineering > School of Mathematics and Statistics > Statistics |
Journal Name: | Open Journal of Statistics |
Publisher: | Scientific Research Publishing, Inc. |
ISSN: | 2161-718X |
ISSN (Online): | 2161-7198 |
Copyright Holders: | Copyright © 2014 The Authors and Scientific Research Publishing Inc. |
First Published: | First published in Open Journal of Statistics 4(5):419-434 |
Publisher Policy: | Reproduced under a Creative Commons License |
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