Estimation of Space-Time Branching Process Models in Seismology Using an EM-Type Algorithm

Maximum likelihood estimation of branching point process models via numerical optimization procedures can be unstable and computationally intensive. We explore an alternative estimation method based on the expectation-maximization algorithm. The method involves viewing the estimation of such branchi...

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Vydané v:	Journal of the American Statistical Association Ročník 103; číslo 482; s. 614 - 624
Hlavní autori:	Veen, Alejandro, Schoenberg, Frederic P
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Alexandria, VA Taylor & Francis 01.06.2008 American Statistical Association Taylor & Francis Ltd
Predmet:	Algorithms Applications Applications and Case Studies Branching process models Disease models Earthquakes Epidemic-type aftershock sequence model Epidemiology Estimate reliability Estimating techniques Estimation Estimation bias Exact sciences and technology Expectation-maximization algorithm General topics Geology Markov processes Mathematics Maximum likelihood Maximum likelihood estimation Maximum likelihood method Modeling Optimization Parametric inference Probability and statistics Probability theory and stochastic processes Regions Regression analysis Robustness (mathematics) Sciences and techniques of general use Seismicity Seismology Simulations Space-time point process models Spacetime Statistical methods Statistics Stochastic processes Tree structures United States > US Southern California Expectation-maximization algorithm Epidemic-type aftershock sequence model Covering problem Optimization method Optimization Hypothesis test Statistical test Branching process models Earthquakes Log likelihood Incomplete information Likelihood function Mathematical programming Epidemic Maximization Numerical model Space time Statistical method Space-time point process models Branching process Numerical analysis Point estimation Point process Maximum likelihood Expectation EM algorithm Application
ISSN:	0162-1459, 1537-274X
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