Multivariate modality inference using Gaussian kernel

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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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
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 (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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