Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Return level estimation from non-stationary spatial data exhibiting multidimensional covariate effects
AU - Jonathan, P.
AU - Randell, D.
AU - Wu, Y.
AU - Ewans, K.
PY - 2014
Y1 - 2014
N2 - Careful modelling of non-stationarity is critical to reliable specification of marine and coastal design criteria. We present a spline based methodology to incorporate spatial, directional, temporal and other covariate effects in extreme value models for environmental variables such as storm severity. For storm peak significant wave height events, the approach uses quantile regression to estimate a suitable extremal threshold, a Poisson process model for the rate of occurrence of threshold exceedances, and a generalised Pareto model for size of threshold exceedances. Multidimensional covariate effects are incorporated at each stage using penalised (tensor products of) B-splines to give smooth model parameter variation as a function of multiple covariates. Optimal smoothing penalties are selected using cross-validation, and model uncertainty is quantified using a bootstrap re-sampling procedure. The method is applied to estimate return values for large spatial neighbourhoods of locations, incorporating spatial and directional effects. Extensions to joint modelling of multivariate extremes, incorporating extremal spatial dependence (using max-stable processes) or more general extremal dependence (using the conditional extremes approach) are outlined. © 2014 Elsevier Ltd.
AB - Careful modelling of non-stationarity is critical to reliable specification of marine and coastal design criteria. We present a spline based methodology to incorporate spatial, directional, temporal and other covariate effects in extreme value models for environmental variables such as storm severity. For storm peak significant wave height events, the approach uses quantile regression to estimate a suitable extremal threshold, a Poisson process model for the rate of occurrence of threshold exceedances, and a generalised Pareto model for size of threshold exceedances. Multidimensional covariate effects are incorporated at each stage using penalised (tensor products of) B-splines to give smooth model parameter variation as a function of multiple covariates. Optimal smoothing penalties are selected using cross-validation, and model uncertainty is quantified using a bootstrap re-sampling procedure. The method is applied to estimate return values for large spatial neighbourhoods of locations, incorporating spatial and directional effects. Extensions to joint modelling of multivariate extremes, incorporating extremal spatial dependence (using max-stable processes) or more general extremal dependence (using the conditional extremes approach) are outlined. © 2014 Elsevier Ltd.
KW - B-spline
KW - Covariate
KW - Extreme
KW - Offshore design
KW - Return value
KW - Storm severity
KW - Interpolation
KW - Offshore structures
KW - Ship propellers
KW - Stability criteria
KW - Uncertainty analysis
KW - Covariates
KW - Storms
KW - bootstrapping
KW - covariance analysis
KW - design
KW - model validation
KW - multivariate analysis
KW - offshore structure
KW - regression analysis
KW - smoothing
KW - spatial data
KW - storm
U2 - 10.1016/j.oceaneng.2014.07.007
DO - 10.1016/j.oceaneng.2014.07.007
M3 - Journal article
VL - 88
SP - 520
EP - 532
JO - Ocean Engineering
JF - Ocean Engineering
SN - 0029-8018
ER -