FN Archimer Export Format
PT J
TI Modeling process asymmetries with Laplace moving average
BT 
AF RAILLARD, Nicolas
   PREVOSTO, Marc
   AILLIOT, Pierre
AS 1:1;2:1;3:2;
FF 1:PDG-REM-RDT-LCSM;2:PDG-REM-RDT-LCSM;3:;
C1 IFREMER, Lab Comportement Struct Mer, Issy Les Moulineaux, France.
   Univ Bretagne Occidentale, Lab Math Bretagne Atlantique, Brest, France.
C2 IFREMER, FRANCE
   UBO, FRANCE
SI BREST
SE PDG-REM-RDT-LCSM
IN WOS Ifremer jusqu'en 2018
   copubli-france
   copubli-univ-france
IF 1.179
TC 2
UR https://archimer.ifremer.fr/doc/00201/31189/29588.pdf
LA English
DT Article
DE ;Laplace moving average;Non-linear time series;FIR estimation;Splines;High-order spectrum;Asymmetries
AB Many records in environmental science exhibit asymmetries: for example in shallow water and with variable bathymetry, the sea wave time series shows front–back asymmetries and different shapes for crests and troughs. In such situation, numerical models are available but their computational cost and complexity are high. A stochastic process aimed at modeling such asymmetries has recently been proposed, the Laplace moving average process, which consists in applying a linear filter on a non-Gaussian noise built using the generalized Laplace distribution. The objective is to propose a new non-parametric estimator for the kernel involved in the definition of this process. Results based on a comprehensive numerical study will be shown in order to evaluate the performances of the proposed method.
PY 2015
PD JAN
SO Computational Statistics & Data Analysis
SN 0167-9473
PU Elsevier Science Bv
VL 81
UT 000343347500003
BP 24
EP 37
DI 10.1016/j.csda.2014.07.010
ID 31189
ER

EF