Density estimation for spatial-temporal models

In this paper a k -nearest neighbor type estimator of the marginal density function for a random field which evolves with time is considered. Considering dependence, the consistency and asymptotic distribution are studied for the stationary and nonstationary cases. In particular, the parametric rate...

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Bibliographic Details
Published in:Test (Madrid, Spain) Spain), 2013-06, Vol.22 (2), p.321-342
Main Authors: Forzani, Liliana, Fraiman, Ricardo, Llop, Pamela
Format: Article
Language:English
Subjects:
Asymptotic properties
Density
Dependent sample
Economics
Estimates
Finance
Insurance
Management
Mathematical functions
Mathematics and Statistics
Original Paper
Random variables
Statistical Theory and Methods
Statistics
Statistics for Business
Studies
Citations: Items that this one cites
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Summary:In this paper a k -nearest neighbor type estimator of the marginal density function for a random field which evolves with time is considered. Considering dependence, the consistency and asymptotic distribution are studied for the stationary and nonstationary cases. In particular, the parametric rate of convergence is proven when the random field is stationary. The performance of the estimator is shown by applying our procedure to a real data example.
ISSN:1133-0686
1863-8260
DOI:10.1007/s11749-012-0313-3