Convergence properties of functional estimates for discrete distributions
Random Structures & Algorithms - Special issue on analysis of algorithms dedicated to Don Knuth on the occasion of his (100)8th birthday
Stylized facts of financial time series and hidden semi-Markov models
Computational Statistics & Data Analysis
Computational issues in parameter estimation for stationary hidden Markov models
Computational Statistics
Selecting hidden Markov model state number with cross-validated likelihood
Computational Statistics
A method of hidden Markov model optimization for use with geophysical data sets
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartIII
Hidden Markov models with arbitrary state dwell-time distributions
Computational Statistics & Data Analysis
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
Editorial: The third special issue on Statistical Signal Extraction and Filtering
Computational Statistics & Data Analysis
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A way of combining a hidden Markov model (HMM) and mutual information analysis is proposed to detect possible precursory signals for earthquakes from Global Positioning System (GPS) data. A non-linear filter, which measures the short-term deformation rate ranges, is introduced to extract anomalous signals from the GPS measurements of ground deformation. An HMM fitted to the filtered GPS measurements can classify the deformation data into different states which form proxies for elements of the earthquake cycle. Mutual information is then used to examine whether any of these states possesses any precursory characteristics. The class of GPS measurements identified by the HMM as having the largest variation of deformation rate shows some precursory information and is hence considered as a ''precursory state''. The performance of possible earthquake forecasts is assessed by comparing a decision rule (based on model characteristics) with the actual outcome.