Document Detail

Nonparametric autocovariance estimation from censored time series by Gaussian imputation.
MedLine Citation:
PMID:  20072705     Owner:  NLM     Status:  Publisher    
One of the most frequently used methods to model the autocovariance function of a second-order stationary time series is to use the parametric framework of autoregressive and moving average models developed by Box and Jenkins. However, such parametric models, though very flexible, may not always be adequate to model autocovariance functions with sharp changes. Furthermore, if the data do not follow the parametric model and are censored at a certain value, the estimation results may not be reliable. We develop a Gaussian imputation method to estimate an autocovariance structure via nonparametric estimation of the autocovariance function in order to address both censoring and incorrect model specification. We demonstrate the effectiveness of the technique in terms of bias and efficiency with simulations under various rates of censoring and underlying models. We describe its application to a time series of silicon concentrations in the Arctic.
Jung Wook Park; Marc G Genton; Sujit K Ghosh
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Publication Detail:
Journal Detail:
Title:  Journal of nonparametric statistics     Volume:  21     ISSN:  1048-5252     ISO Abbreviation:  -     Publication Date:  2009 Feb 
Date Detail:
Created Date:  2010-1-14     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101490981     Medline TA:  J Nonparametr Stat (Print)     Country:  -    
Other Details:
Languages:  ENG     Pagination:  241-259     Citation Subset:  -    
Discovery Biometrics, GlaxoSmithKline, NC, USA.
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Grant Support
R01 ES014843-02//NIEHS NIH HHS

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