KERNEL DENSITY-ESTIMATION FOR LINEAR-PROCESSES - ASYMPTOTIC NORMALITYAND OPTIMAL BANDWIDTH DERIVATION

Authors
HALLIN M TRAN LT
Citation
M. Hallin et Lt. Tran, KERNEL DENSITY-ESTIMATION FOR LINEAR-PROCESSES - ASYMPTOTIC NORMALITYAND OPTIMAL BANDWIDTH DERIVATION, Annals of the Institute of Statistical Mathematics, 48(3), 1996, pp. 429-449
Citations number
25
Categorie Soggetti
Statistic & Probability",Mathematics,"Statistic & Probability
Journal title
Annals of the Institute of Statistical Mathematics → ACNP
ISSN journal
00203157
Volume
48
Issue
3
Year of publication
1996
Pages
429 - 449
Database
ISI
SICI code
0020-3157(1996)48:3<429:KDFL-A>2.0.ZU;2-R
Abstract
The problem of estimating the marginal density of a linear process by kernel methods is considered. Under general conditions, kernel density estimators are shown to be asymptotically normal. Their limiting cova riance matrix is computed. We also find the optimal bandwidth in the s ense that it asymptotically minimizes the mean square error of the est imators. The assumptions involved are easily verifiable.