%0 Journal Article
%T Multivariate Modality Inference Using  Gaussian Kernel
%A Yansong Cheng
%A Surajit Ray
%J Open Journal of Statistics
%P 419-434
%@ 2161-7198
%D 2014
%I Scientific Research Publishing
%R 10.4236/ojs.2014.45041
%X <p>
	<a></a><a></a>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 <em>modal clustering</em> 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.
</p>
%K Modality
%K Kernel Density Estimate
%K Mode
%K Clustering
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=49087