Bandwidth selectors for multivariate kernel density estimation
By Tarn Dunong. Bulletin of the Australian Mathematical Society, 2005
48 citations recorded by the source corpus.
Kernel density estimation is an important data smooting technique. It has been applied most successfully for univariate data whilst for multivariate date its development and implementation have been relatively limited. The performance of kernel density estimators depends crucially on the bandwidth selection. Bandwidth selection in the univariate case involves selecting a scalar parameter which controls the amount of smoothing. In the multivariate case, the bandwidth matrix controls both the degree and direction of smoothing so its selection is more difficult. So far most of the research effort has been expended on automatic, data-driven selectors for univariate data. There is, on the other hand, a relative paucity of multivariate counterparts. Most of these multivariate bandwidth selectors are focused on the restricted case of diagonal matrices. In this thesis practical algorithms are constructed, with supporting theoretical justifications, for unconstrained bandwidth matrices.
Subfields: Artificial Intelligence, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/eed75e2849aa8f21360c82836e66fb08b9fca93b
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Citations in the corpus
- Seismic fragility curves for structures using non-parametric representations (2017, background): “ XN i=1 1 (2ˇ)1=2 exp " 1 2 x x i h 2 # : (14) In contrast, the choice of the bandwidth his crucial since an inappropriate value of hcan lead to an oversmoothed or undersmoothed PDF estimate [41]. Kernel density estimation may be extended to a random vector X2Rdgiven an i.i.d sample fx 1;:::;”
- Population-specific home ranges and migration timing of Pacific Arctic beluga whales (Delphinapterus leucas) (2014, background): “Migratory movements are considered to be persistent, somewhat oriented or unidirectional, feature displacement between distinct regions, and more rapid than movement characteristic of focused concentration in a restricted area (Dingle 1996; Stern 2002).”