Model Selection and Semiparametric Inference for Bivariate Failure-Time Data

By Weijing Wang, Martin T. Wells. 2000

265 citations recorded by the source corpus.

Subfields: Demography, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a1eae56cbc168df0a6a09aa41fc2850e8a4a7c5b

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Citations in the corpus

Hierarchical Kendall copulas: Properties and inference (2012, background): “l. (1996). Other accounts on it can be found, amongst others, in Imlahi et al. (1999), Genest and Rivest (2001) and Nelsen et al. (2003) as well as in the copula goodness-of-t literature (see, e.g., Wang and Wells (2000) and Genest et al. (2006)). It has been shown by Genest et al. (1995) that the o”
Modelling stochastic mortality for dependent lives (2008, unknown): “In Section 3 we describe a copula calibration and selection methodology, consistent with the copula class suggested above, and originally proposed by Wang and Wells (2000b).”
Copula goodness-of-fit testing: an overview and power comparison (2009, methodology): “𝒜 4 : Based on the empirical copula and the cdf of the copula function, proposed by Genest and Rivest (1993) , Wang and Wells (2000) , Savu and Trede (2004) and Genest, Quessy, and Remillard (2006a) .”
Estimation of Kendall's tau under censoring (2000, background): “Parameters in these models can then be identified via τ (see Genest (1987), Oakes (1989), Genest and Rivest (1993), Wang and Wells (2000)).”
Estimating the success of re-identifications in incomplete datasets using generative models (2019, methodology): “Copulas have been used to study a wide range of dependence structures in ﬁ nance 44 , geology 45 , and biomedicine 46 and allow us to model the density of X by specifying separately the marginal distributions, easy to infer from limited samples, and the dependency structure.”
Goodness‐of‐fit Procedures for Copula Models Based on the Probability Integral Transformation (2006, methodology): “Results for the Snn statistic of Wang & Wells (2000) with n ¼ 0 were also added for comparison purposes.”
Modelling the statistical dependence of rainfall event variables through copula functions (2011, background): “are classified by the Authors in three main categories (i) those that can be utilized only for a specific copula family (Shih, 1998; Malevergne and Sornette, 2003), (ii) those that have a general applicability but involve important subjective choices for their implementation, such as a parameter (Wa”
Applying copula models to individual claim loss reserving methods (2010, background): “Apart from the bivariate right censoring (cf. Shih and Louis, 1995; Wang and Wells, 2000), the existing censoring schemes in copula models can be classified into three categories: (i) One variable is (independently or dependently) censored by the other variable, such as dependent censoring in…”
An Overview of the Goodness-of-Fit Test Problem for Copulas (2012, methodology): “For instance, [90] introduced a test statistics based on the L2 norm of Kn.”
Copulas‐based risk analysis for inter‐seasonal combinations of wet and dry conditions under a changing climate (2018, methodology): “The optimal copulas are determined mainly dependent on a goodness-of-fit test based on Rosenblatt’s transform which derives a Cramér–von Mises statistic (Wang and Wells, 2000; Genest et al., 2009).”
On the multivariate probability integral transformation (2001, background): “…−1{ (x) + (y)} for some generator satisfying condition (ii) in Example 1 below, (v) = (v) ≡ (v)= ′(v) is de?ned almost everywhere as the familiar ratio that characterizes a copula uniquely within that class (see, for instance, Genest and MacKay, 1986; Genest and Rivest, 1993; Wang and Wells, 2000).”