Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jspi.2011.02.006
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dc.titleSuper efficient frequency estimation
dc.contributor.authorKundu, D.
dc.contributor.authorBai, Z.
dc.contributor.authorNandi, S.
dc.contributor.authorBai, L.
dc.date.accessioned2014-10-28T05:15:33Z
dc.date.available2014-10-28T05:15:33Z
dc.date.issued2011-08
dc.identifier.citationKundu, D., Bai, Z., Nandi, S., Bai, L. (2011-08). Super efficient frequency estimation. Journal of Statistical Planning and Inference 141 (8) : 2576-2588. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jspi.2011.02.006
dc.identifier.issn03783758
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105397
dc.description.abstractIn this paper we propose a modified Newton-Raphson method to obtain super efficient estimators of the frequencies of a sinusoidal signal in presence of stationary noise. It is observed that if we start from an initial estimator with convergence rate Op(n-1) and use Newton-Raphson algorithm with proper step factor modification, then it produces super efficient frequency estimator in the sense that its asymptotic variance is lower than the asymptotic variance of the corresponding least squares estimator. The proposed frequency estimator is consistent and it has the same rate of convergence, namely Op(n-3/2), as the least squares estimator. Monte Carlo simulations are performed to observe the performance of the proposed estimator for different sample sizes and for different models. The results are quite satisfactory. One real data set has been analyzed for illustrative purpose. © 2011 Elsevier B.V.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.jspi.2011.02.006
dc.sourceScopus
dc.subjectAsymptotic distributions
dc.subjectLeast squares estimators
dc.subjectModified Newton-Raphson
dc.subjectSinusoidal signals
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1016/j.jspi.2011.02.006
dc.description.sourcetitleJournal of Statistical Planning and Inference
dc.description.volume141
dc.description.issue8
dc.description.page2576-2588
dc.description.codenJSPID
dc.identifier.isiut000291067400007
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