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Multivariate Modality Inference Using Gaussian Kernel
Quantitative Science, GlaxoSmithKline, King of Prussia, PA, USA
School of Mathematics and Statistics, Glasgow University, Glasgow, UK
- 1 Quantitative Science, GlaxoSmithKline, King of Prussia, PA, USA
- 2 School of Mathematics and Statistics, Glasgow University, Glasgow, UK
Open Journal of Statistics·Volume 04 (2014)·Pages 419–434·Published 7 August 2014·DOI10.4236/ojs.2014.45041
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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.
KeywordsModalityKernel Density EstimateModeClustering
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