A kolmogorov‐smirnov type test for positive quadrant dependence

By Olivier Scaillet. 2005

123 citations recorded by the source corpus.

The author considers a consistent, Kolmogorov‐Smirnov type of test of the complete set of restrictions that relate to the copula representation of positive quadrant dependence. For such a test, he proposes and justifies inference relying on a simulation‐based multiplier method and a bootstrap method. He also explores the finite‐sample behaviour of both methods with Monte Carlo experiments. A first empirical illustration is given for American insurance claim data. A second one examines the presence of positive quadrant dependence in life expectancies at birth of males and females across countries.

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Citations in the corpus

Goodness-of-Fit Tests for Copulas of Multivariate Time Series (2017, unknown): “Even if the law of G depends on the unknown copula C, it is easy to simulate independent copies, using a multiplier method adapted from Scaillet (2005) and Rémillard and Scaillet (2009).”
Large‐sample tests of extreme‐value dependence for multivariate copulas (2011, methodology): “…suggesting two candidate test statistics based onDr,n, let us first explain how, for large n, approximate independent copies of Dr can be obtained by means of a multiplier technique initially proposed in Scaillet (2005) and Rémillard & Scaillet (2009), and recently revisited in Segers (2011).”
Modeling Multivariate Distributions with Continuous Margins Using the copula R Package (2010, methodology): “Inspired by the seminal work of Scaillet (2005) and R´emillard and Scaillet (2009), a valid and much faster alternative to the previously presented parametric bootstrap-based procedure was recently proposed in Kojadinovic et al. (2010).”
A note on bootstrap approximations for the empirical copula process (2010, unknown): “The upper index pdm in (6) denotes the fact that these authors are using estimates of the partial derivatives and a multiplier concept.”
FAST LARGE-SAMPLE GOODNESS-OF-FIT TESTS FOR COPULAS (2011, unknown): “…of statistics in other types of tests based on empirical processes; see e.g., Lin, Fleming, and Wei (1994) and Fine, Yan, and Kosorok (2004) for applications in survival analysis or, more recently, Scaillet (2005), and Rémillard and Scaillet (2009) for applications in the copula modeling context.”
A dependent multiplier bootstrap for the sequential empirical copula process under strong mixing (2013, unknown): “pirical copula process Cn(0,1,·,·) were proposed in the literature, ranging from the multinomial bootstrap of Fermanian, Radulovi´c and Wegkamp [21] to the multiplier technique introduced in Scaillet [42] and investigated further in R´emillard and Scaillet [39]. Their ﬁnite-sample properties were co”
New estimators of the Pickands dependence function and a test for extreme-value dependence (2011, background): “As pointed out by Scaillet (2005), this property is equivalent to the concept of positive quadrant dependence, that is, P(X ≤ x,Y ≤ y)≥ P(X ≤ x)P(Y ≤ y) ∀(x, y) ∈R(2).”
Asymptotics of empirical copula processes under non-restrictive smoothness assumptions (2010, background): “(3.7) In ( ∞([0,1]d))2, we have by Lemma A.1 in Rémillard and Scaillet [24], (αn,α ′ n) (α,α′) (n → ∞), (3.8) where α′ is an independent copy of α.”