Composite likelihood estimation for the Brown–Resnick process

By Raphaël Huser, A. C. Davison. 2013

153 citations recorded by the source corpus.

Subfields: Demography, Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/247b1ad48bbb032994b71d6e6b05fcbfffce532d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Demography

In Economics and Econometrics

In Finance

Citations in the corpus

An M‐estimator of spatial tail dependence (2014, background): “In Huser and Davison (2013) the following representation is given for (cid:96) ( x 1 , . . . , x d ; θ ) for general d .”
Space–time modelling of extreme events (2012, methodology): “The full likelihood cannot be obtained analytically for most max-stable processes (but see Genton et al., 2011; Huser and Davison, 2013).”
Likelihood estimators for multivariate extremes (2014, background): “However, the former are more variable than the latter, and require a special treatment of uncertainty (Cox and Reid 2004; Padoan et al. 2010; Davis and Yau 2011; Huser and Davison 2013).”
A continuous updating weighted least squares estimator of tail dependence in high dimensions (2016, background): “From Huser and Davison (2013), we obtain the following representation for the extremal coefficients J in (2.”
The normal law under linear restrictions: simulation and estimation via minimax tilting (2016, background): “…random ﬁelds (Bolin and Lindgren, 2015); inference for spacial processes (Wadsworth and Tawn, 2014); likelihood estimation for max-stable processes (Huser and Davison, 2013; Genton et al., 2011); computation of simultaneous conﬁdence bands (Aza¨ıs et al., 2010); uncertainty regions for latent…”
Spatial extremes: Max-stable processes at work (2013, background): “In this section we focus on the pairwise likelihood but composite likelihoods based on triplets are sometimes possible Genton et al. (2011); Huser and Davison (2013).”
Multivariate peaks over thresholds models (2016, unknown): “The integral in the denominator can be expressed as a sum of d components, each of which involves a (d − 1)-dimensional normal cdf, see Huser and Davison (2013).”
Non-Stationary Dependence Structures for Spatial Extremes (2014, methodology): “(4) The exponent measure has a closed-form formula for specific choices of W (s); see, e.g., Schlather (2002), Genton et al. (2011), Huser and Davison (2013), and Opitz (2013).”
Efficient inference and simulation for elliptical Pareto processes (2013, methodology): “Hence, inference is usually made using a less efficient composite likelihood approach (Padoan et al., 2010; Genton et al., 2011; Huser & Davison, 2013a).”
Bridging asymptotic independence and dependence in spatial extremes using Gaussian scale mixtures (2016, methodology): “Full likelihoods can only be calculated in small dimensions, which led to the use of less efficient inference techniques, such as composite likelihoods (Padoan et al., 2010; Huser and Davison, 2013; Castruccio et al., 2016).”
Threshold modeling of extreme spatial rainfall (2013, methodology): “Taking 1⁄4 2 is equivalent to using the Smith model with a symmetric covariance matrix R [Huser and Davison, 2013a]. [31] Schlather [2002] proposes a third model, taking W(x) proportional to the positive part of a stationary centered Gaussian process with unit variance and correlation function h ð Þ”
Functional peaks‐over‐threshold analysis (2020, methodology): “Alternatives involve censoring of low components (e.g., Wadsworth & Tawn, 2014), composite likelihoods (Castruccio et al., 2016; Huser & Davison, 2013; Padoan et al., 2010) or M-estimation using pairwise tail indexes (Einmahl et al., 2016a,b).”
Factor Copula Models for Replicated Spatial Data (2015, methodology): “We next show that the stable upper tail dependence function (Segers, 2012) of the limiting extreme-value copula in the exponential factor model (Example 2) corresponds to the Hüsler and Reiss (1989) distribution, which has been widely used as a flexible family to model extreme events (Davison et al.”