Some statistical methods for random process data from seismology and neurophysiology

DOI10.1214/AOS/1176350689zbMath0637.62089OpenAlexW4236287093MaRDI QIDQ1098532

David R. Brillinger

Publication date: 1988

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/aos/1176350689

zbMATH Keywords

system identificationtime seriessingular value decompositionFourier analysisfrequency estimationpoint processesmaximum likelihooddeconvolutionnonlinear regressionaveragingbinary dataexploration seismologyneurophysiologycomplex demodulationstructural modelingreflection seismologysignal estimationfree oscillations of Earthprobit analysisanalysis of evoked responsesarray dataassociation measurementcausal connection assessmentestimation of fault-plane parametersneuronal signalingquantification of earthquakesspatial-temporal processes

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Seismology (including tsunami modeling), earthquakes (86A15) Applications of statistics (62P99) Inference from stochastic processes (62M99) Physiological, cellular and medical topics (92Cxx)

Related Items (8)

Estimating the interaction graph of stochastic neuronal dynamics by observing only pairs of neurons ⋮ Optimal investment, consumption, and life insurance strategies under a mutual-exciting contagious market ⋮ STOCHASTIC MODELING AND IDENTIFICATION OF SEISMIC RECORDS BASED ON ESTABLISHED DETERMINISTIC FORMULATIONS ⋮ Testing for self-excitation in jumps ⋮ Special section on statistics in neuroscience ⋮ On Firing Rate Estimation for Dependent Interspike Intervals ⋮ Semi-parametric dynamic time series modelling with applications to detecting neural dynamics ⋮ Estimating the number of neurons in multi-neuronal spike trains

Uses Software

• HSL

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