caa a22 4500 605463840 CHVBK 20210128100255.0 cr unu---uuuuu 210128e20151201xx s 000 0 eng 10.1007/s10687-015-0227-z doi (NATIONALLICENCE)springer-10.1007/s10687-015-0227-z Slepian noise approach for gaussian and Laplace moving average processes [Elektronische Daten] [K. Podgórski, I. Rychlik, J. Wallin] Slepian models are derived for a stochastic process observed at level crossings of a moving average driven by a gaussian or Laplace noise. In particular, a Slepian model for the noise - the Slepian noise - is developed. For Laplace moving average process a method of sampling from the Slepian noise is also obtained by a Gibbs sampler. This facilitates comparison of behavior at crossing of a level between a gaussian process and a non-gaussian one and allows to study a random processes sampled at crossings of a non-gaussian moving average process. In a numerical study based on the method it is observed that the behavior of a non-gaussian moving average process at high level crossings is fundamentally different from that for the gaussian case, which is in line with some recent theoretical results on the subject. Springer Science+Business Media New York, 2015 Rice formula nationallicence Level crossings nationallicence Generalized Laplace distribution nationallicence Moving average process nationallicence Extreme episodes nationallicence Tilted Rayleigh distribution nationallicence Generalized inverse gaussian distribution nationallicence Podgórski K. Statistics, Lund University, Lund, Sweden aut Rychlik I. Mathematical Statistics, Chalmers University, Gothenburg, Sweden aut Wallin J. Mathematical Statistics, Chalmers University, Gothenburg, Sweden aut Extremes Springer US; http://www.springer-ny.com 18/4(2015-12-01), 665-695 1386-1999 18:4<665 2015 18 10687 https://doi.org/10.1007/s10687-015-0227-z text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10687-015-0227-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Podgórski K. Statistics, Lund University, Lund, Sweden aut NATIONALLICENCE 700 1- Rychlik I. Mathematical Statistics, Chalmers University, Gothenburg, Sweden aut NATIONALLICENCE 700 1- Wallin J. Mathematical Statistics, Chalmers University, Gothenburg, Sweden aut NATIONALLICENCE 773 0- Extremes Springer US; http://www.springer-ny.com 18/4(2015-12-01), 665-695 1386-1999 18:4<665 2015 18 10687