On the Amount of Noise Inherent in Bandwidth Selection for a Kernel Density Estimator
نویسندگان
چکیده
منابع مشابه
Kernel Estimator and Bandwidth Selection for Density and its Derivatives
In statistics, the univariate kernel density estimation (KDE) is a non-parametric way to estimate the probability density function f(x) of a random variable X, is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample. This techniques are widely used in various inference procedures such as signal processing, data mining and econometric...
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Among available bandwidths for kernel density estimators, the critical bandwidth is a data-driven one, which satisfies a constraint on the number of modes of the estimated density. When using a random bandwidth, it is of particular interest to show that it goes toward 0 in probability when the sample size goes to infinity. Such a property is important to prove satisfying asymptotic results abou...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 1987
ISSN: 0090-5364
DOI: 10.1214/aos/1176350259