Open Journal of Statistics

Volume 4, Issue 5 (August 2014)

ISSN Print: 2161-718X   ISSN Online: 2161-7198

Google-based Impact Factor: 0.82  Citations  h5-index & Ranking

Multivariate Modality Inference Using Gaussian Kernel

HTML  Download Download as PDF (Size: 5248KB)  PP. 419-434  
DOI: 10.4236/ojs.2014.45041    3,757 Downloads   4,801 Views  Citations


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.

Share and Cite:

Cheng, Y. and Ray, S. (2014) Multivariate Modality Inference Using Gaussian Kernel. Open Journal of Statistics, 4, 419-434. doi: 10.4236/ojs.2014.45041.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.