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KERNEL DENSITY ESTIMATION WHEN THE BANDWIDTH IS LARGE
Author(s) -
Jones M.C.
Publication year - 1993
Publication title -
australian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 0004-9581
DOI - 10.1111/j.1467-842x.1993.tb01339.x
Subject(s) - multivariate kernel density estimation , kernel density estimation , variable kernel density estimation , bandwidth (computing) , kernel (algebra) , density estimation , mean squared error , mathematics , mean shift , estimation , computer science , statistics , algorithm , kernel method , pattern recognition (psychology) , artificial intelligence , discrete mathematics , support vector machine , engineering , telecommunications , estimator , systems engineering
Summary The performance of kernel density estimation, in terms of mean integrated squared error, is investigated in the opposite of the usual situation, namely when the bandwidth is large. This affords noteworthy insights including the special role taken by the normal density function as kernel and a tie‐in with ‘semiparametric’ approaches to density estimation.