Earthquake source parameters of the 2009 Mw 7.8 Fiordland (New Zealand) earthquake from L-band InSAR observations

The 2009 MW7.8 Fiordland (New Zealand) earthquake is the largest to have occurred in New Zealand since the 1931 Mw 7.8 Hawke’s Bay earthquake, 1 000 km to the northwest. In this paper two tracks of ALOS PALSAR interferograms (one ascending and one descending) are used to determine fault geometry and slip distribution of this large earthquake. Modeling the event as dislocation in an elastic half-space suggests that the earthquake resulted from slip on a SSW-NNE orientated thrust fault that is associated with the subduction between the Pacific and Australian Plates, with oblique displacement of up to 6.3 m. This finding is consistent with the preliminary studies undertaken by the USGS using seismic data

Plasma fluctuations as Markovian noise

Noise theory is used to study the correlations of stationary Markovian fluctuations that are homogeneous and isotropic in space. The relaxation of the fluctuations is modeled by the diffusion equation. The spatial correlations of random fluctuations are modeled by the exponential decay. Based on these models, the temporal correlations of random fluctuations, such as the correlation function and the power spectrum, are calculated. We find that the diffusion process can give rise to the decay of the correlation function and a broad frequency spectrum of random fluctuations. We also find that the transport coefficients may be estimated by the correlation length and the correlation time. The theoretical results are compared with the observed plasma density fluctuations from the tokamak and helimak experiments.Physic

Estimation and Testing for Unit Root Processes with GARCH(1,1) Errors: Theory and Monte Carlo Evidence,

Least squares (LS) and maximum likelihood (ML) estimation are considered for unit root processes with GARCH (1, 1) errors. The asymptotic distributions of LS and ML estimators are derived under the condition alpha + beta

"Estimation and Testing for Unit Root Processes with GARCH (1, 1) Errors: Theory and Monte Carlo Evidence"

Least squares (LS) and maximum likelihood (ML) estimation are con-sidered for unit root processes with GARCH (1, 1) errors. The asymp-totic distributions of LS and ML estimators are derived under the con-dition ƒ¿ + ƒÀ

A Survey of Recent Theoretical Results for Time Series Models with GARCH Errors,

This paper provides a review of some recent theoretical results for time series models with GARCH errors, and is directed towards practitioners. Starting with the simple ARCH model and proceeding to the GARCH model, some results for stationary and nonstationary ARMA-GARCH are summarized. Various new ARCH-type models, including double threshold ARCH and GARCH, ARFIMA-GARCH, CHARMA and vector ARMA-GARCH, are also reviewed.