TITLE:
Multivariate Modality Inference Using Gaussian Kernel
AUTHORS:
Yansong Cheng, Surajit Ray
KEYWORDS:
Modality, Kernel Density Estimate, Mode, Clustering
JOURNAL NAME:
Open Journal of Statistics,
Vol.4 No.5,
August
25,
2014
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.