Multivariate Modality Inference Using  Gaussian Kernel

Yansong Cheng; Surajit Ray

doi:10.4236/ojs.2014.45041

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OJS> Vol.4 No.5, August 2014

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Multivariate Modality Inference Using Gaussian Kernel ()

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DOI: 10.4236/ojs.2014.45041 3,239 Downloads 3,740 Views Citations

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Yansong Cheng, Surajit Ray

Affiliation(s)

Quantitative Science, GlaxoSmithKline, King of Prussia, PA, USA.
School of Mathematics and Statistics, Glasgow University, Glasgow, UK.

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.

KEYWORDS

Modality, Kernel Density Estimate, Mode, Clustering

Cite this paper

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.

Conflicts of Interest

The authors declare no conflicts of interest.

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