Weak Convergence of Empirical Copula Processes

By Jean-David Fermanian, D. Radulovic, Marten H. Wegkamp. 2004

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Citations in the corpus

Copula-Based Regression Estimation and Inference (2013, methodology): “The last equality follows by applying Theorem 6 in Fermanian, Radulovi, and Wegkamp (2004) to the multivariate rank order statistics n − 1 (cid:16) n i = 1 M 2 ( ˆ F 0 ,n ( Y i ) , ˆ F 1 ,n ( X 1 ,i )).”
Copulas in Econometrics (2014, background): “The majority of the literature considers parametric copula models (for nonparametric estimation of copulas, see Genest & Rivest 1993 and Capéraà et al. 1997 for i.i.d. data and Fermanian & Scaillet 2003, Fermanian et al. 2004, Sancetta & Satchell 2004, and Ibragimov 2009 for time series data).”
Quantile Spectral Processes: Asymptotic Analysis and Inference (2014, unknown): “able under general kinds of dependence, is based on their representation as functionals of (weighted) empirical copula processes and was considered, for instance, in Fermanian, Radulovi´c and Wegkamp [14], Berghaus, Bu¨cher and Volgushev [2]. Despite the great success of the L2-periodogram in time s”
On the empirical multilinear copula process for count data (2014, result): “n almost surely. This also implies that Cbz is asymptotically equivalent to other versions of the empirical copula commonly used in the literature; see, for example, Fermanian, Radulovi´c and Wegkamp [6]. To ease the notation, it will be assumed henceforth, without loss of generality, that X 1,...,X”
On quadratic functionals of the Brownian sheet and related processes (2006, unknown): “For example, Fermanian, Radulović and Wegkamp, [26], show that the weak convergence of Γn(·, ·) to Γ(·, ·) holds on [0, 1](2) when C(·, ·) has continuous partial derivatives on [0, 1](2).”
Nonparametric estimation of copula functions for dependence modelling (2007, methodology): “Gijbels & Mielnicnuk (1990) proposed a kernel estimator for a bivariate copula density, which is employed by Fermanian (2005) in a goodness-of-fit test.”
Probit Transformation for Nonparametric Kernel Estimation of the Copula Density (2014, background): “The related empirical copula process was studied further in Fermanian et al (2004), Tsukuhara (2005), Segers (2012) and B¨ucher and Volgushev (2013), and turns out to be the cornerstone of a variety of nonparametric copula-based procedures, see e.g. Genest and R´emillard (2004), Genest et al…”
Nonparametric inference on multivariate versions of Blomqvist’s beta and related measures of tail dependence (2007, methodology): “The estimators we utilize are expressed by the empirical copula process, see Rüschendorf (1976), Deheuvels (1979), Fermanian et al. (2004) or Tsukahara (2005) and related techniques are used in order to establish asymptotic normality and strong consistency.”
FAST LARGE-SAMPLE GOODNESS-OF-FIT TESTS FOR COPULAS (2011, unknown): “The insurance data of Frees and Valdez (1998) are frequently used for illustration purposes in copula modeling (see e.g. Ben Ghorbal, Genest, and Nešlehová, 2009; Genest, Quessy, and Rémillard, 2006; Klugman and Parsa, 1999). The two variables of interest are an indemnity payment and the correspondi”
Multivariate Extensions of Spearman's Rho and Related Statistics (2007, background): “Weak convergence takes place in ∞ ([0] [1] d ), cf. Fermanian, Radulovi´c, and Wegkamp (2004).”
Statistics of Extremes (2004, background): “The estimator γ̂k(c, λv) is in fact the one proposed by Yun (2002), including as special cases the ones by Pickands (1975) [c = v = 1/2], Pereira (1994) and Fraga Alves (1995) [c = v], and Yun (2000b) [1/4 < c < 1 and v = (4c)−1].”
COPULA-BASED CHARACTERIZATIONS FOR HIGHER ORDER MARKOV PROCESSES (2009, background): “Fermanian, Radulović, and Wegkamp (2004) established weak convergence of empirical copula processes in the case of independently observed vectors with dependent components.”