Article (Scientific journals)
A Bayesian Monte Carlo Markov Chain Method for Parameter Estimation of Fractional Differenced Gaussian Processes
Olivares Pulido, German; Teferle, Felix Norman
2013In IEEE Transactions on Signal Processing, 61 (9), p. 2405-2412
Peer reviewed
 

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Keywords :
Monte Carlo; Gauss Markov Chain; Geodetic Time Series
Abstract :
[en] We present a Bayesian Monte Carlo Markov Chain method to simultaneously estimate the spectral index and power amplitude of a fractional differenced Gaussian process at low frequency, in presence of white noise, and a linear trend and periodic signals. This method provides a sample of the likelihood function and thereby, using Monte Carlo integration, all parameters and their uncertainties are estimated simultaneously. We test this method with simulated and real Global Positioning System height time series and propose it as an alternative to optimization methods currently in use. Furthermore, without any mathematical proof, the results from the simulations suggest that this method is unaffected by the stationary regime and hence, can be used to check whether or not a time series is stationary.
Disciplines :
Earth sciences & physical geography
Author, co-author :
Olivares Pulido, German ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
Teferle, Felix Norman ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
Language :
English
Title :
A Bayesian Monte Carlo Markov Chain Method for Parameter Estimation of Fractional Differenced Gaussian Processes
Publication date :
2013
Journal title :
IEEE Transactions on Signal Processing
Publisher :
IEEE
Volume :
61
Issue :
9
Pages :
2405-2412
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 23 April 2013

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