The Annals of Statistics

On bandwidth choice for density estimation with dependent data

Peter Hall, Soumendra Nath Lahiri, and Young K. Truong

Full-text: Open access

Abstract

We address the empirical bandwidth choice problem in cases where the range of dependence may be virtually arbitrarily long. Assuming that the observed data derive from an unknown function of a Gaussian process, it is argued that, unlike more traditional contexts of statistical inference, in density estimation there is no clear role for the classical distinction between short- and long-range dependence. Indeed, the "boundaries" that separate different modes of behaviour for optimal bandwidths and mean squared errors are determined more by kernel order than by traditional notions of strength of dependence, for example, by whether or not the sum of the covariances converges. We provide surprising evidence that, even for some strongly dependent data sequences, the asymptotically optimal bandwidth for independent data is a good choice. A plug-in empirical bandwidth selector based on this observation is suggested. We determine the properties of this choice for a wide range of different strengths of dependence. Properties of cross-validation are also addressed.

Article information

Source
Ann. Statist., Volume 23, Number 6 (1995), 2241-2263.

Dates
First available in Project Euclid: 15 October 2002

Permanent link to this document
https://projecteuclid.org/euclid.aos/1034713655

Digital Object Identifier
doi:10.1214/aos/1034713655

Mathematical Reviews number (MathSciNet)
MR1389873

Zentralblatt MATH identifier
0854.62039

Subjects
Primary: 62G07: Density estimation
Secondary: 62M10: Time series, auto-correlation, regression, etc. [See also 91B84]

Keywords
Bandwidth choice cross-validation density estimation Gaussian process integrated squared error kernel methods long-range dependence mean integrated squared error plug-in rule short-range dependence window width

Citation

Hall, Peter; Lahiri, Soumendra Nath; Truong, Young K. On bandwidth choice for density estimation with dependent data. Ann. Statist. 23 (1995), no. 6, 2241--2263. doi:10.1214/aos/1034713655. https://projecteuclid.org/euclid.aos/1034713655


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