{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Deniz Gencaga", "hasEmail": "mailto:dgencaga@gmail.com"}, "description": "In the literature, impulsive signals are mostly modeled by symmetric alpha-stable processes. To represent their temporal dependencies, usually autoregressive models with time-invariant coefficients are utilized. We propose a general sequential Bayesian modeling methodology where both unknown autoregressive coefficients and distribution parameters can be estimated successfully, even when they are time-varying. In contrast to most work in the literature on signal processing with alpha-stable distributions, our work is general and models also skewed alpha-stable processes. Successful performance of our method is demonstrated by computer simulations. We support our empirical results by providing posterior Cramer\u2013Rao lower bounds. The proposed method is also tested on a practical application where seismic data events are modeled.", "distribution": [{"@type": "dcat:Distribution", "description": "Modeling of non-stationary autoregressive alpha-stable processes by particle filters", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/seismic.pdf", "format": "PDF", "mediaType": "application/pdf", "title": "seismic.pdf"}], "identifier": "DASHLINK_208", "issued": "2010-09-22", "keyword": ["ames", "dashlink", "nasa"], "landingPage": "https://c3.nasa.gov/dashlink/resources/208/", "modified": "2025-03-31", "programCode": ["026:029"], "publisher": {"@type": "org:Organization", "name": "Dashlink"}, "title": "Modeling of non-stationary autoregressive alpha-stable processe"}