The Annals of Statistics
- Ann. Statist.
- Volume 18, Number 2 (1990), 873-890.
Data-Driven Bandwidth Choice for Density Estimation Based on Dependent Data
The bandwidth selection problem in kernel density estimation is investigated in situations where the observed data are dependent. The classical leave-out technique is extended, and thereby a class of cross-validated bandwidths is defined. These bandwidths are shown to be asymptotically optimal under a strong mixing condition. The leave-one out, or ordinary, form of cross-validation remains asymptotically optimal under the dependence model considered. However, a simulation study shows that when the data are strongly enough correlated, the ordinary version of cross-validation can be improved upon in finite-sized samples.
Ann. Statist., Volume 18, Number 2 (1990), 873-890.
First available in Project Euclid: 12 April 2007
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Secondary: 62G20: Asymptotic properties 62M99: None of the above, but in this section 62M10: Time series, auto-correlation, regression, etc. [See also 91B84] 60G10: Stationary processes 60G35: Signal detection and filtering [See also 62M20, 93E10, 93E11, 94Axx]
Hart, Jeffrey D.; Vieu, Philippe. Data-Driven Bandwidth Choice for Density Estimation Based on Dependent Data. Ann. Statist. 18 (1990), no. 2, 873--890. doi:10.1214/aos/1176347630. https://projecteuclid.org/euclid.aos/1176347630