Results 11 to 20 of about 104 (95)
Dependence Modeling, 2013 We study the impact of certain transformations within the class of Archimedean copulas. We give some admissibility conditions for these transformations, and define some equivalence classes for both transformations and generators of Archimedean copulas ...
Di Bernardino Elena, Rullière Didier
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Large deviations for exchangeable observations with applications
International Journal of Mathematics and Mathematical Sciences, Volume 2004, Issue 55, Page 2947-2958, 2004., 2004 We first prove some large deviation results for a mixture of i.i.d. random variables. Compared with most of the known results in the literature, our results are built on relaxing some restrictive conditions that may not be easy to be checked in certain typical cases.
Jinwen Chen
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A survey of limit laws for bootstrapped sums
International Journal of Mathematics and Mathematical Sciences, Volume 2003, Issue 45, Page 2835-2861, 2003., 2003 Concentrating mainly on independent and identically distributed (i.i.d.) real‐valued parent sequences, we give an overview of first‐order limit theorems available for bootstrapped sample sums for Efron′s bootstrap. As a light unifying theme, we expose by elementary means the relationship between corresponding conditional and unconditional bootstrap ...
Sándor Csörgő, Andrew Rosalsky
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Empirical likelihood for quantile regression models with response data missing at random
Open Mathematics, 2017 This paper studies quantile linear regression models with response data missing at random. A quantile empirical-likelihood-based method is proposed firstly to study a quantile linear regression model with response data missing at random.
Luo S., Pang Shuxia
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Conditional Density Kernel Estimation Under Random Censorship for Functional Weak Dependence Data
Journal of Mathematics, Volume 2025, Issue 1, 2025.The primary objective of this research is to investigate the asymptotic properties of the conditional density nonparametric estimator. The main areas of focus are the estimator’s consistency (with rates), including those involving censored data and quasi‐associated dependent variables, as well as its performance when the covariate is functional in ...
Hamza Daoudi +4 more
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Variable importance for causal forests: breaking down the heterogeneity of treatment effects
Journal of Causal InferenceCausal random forests provide efficient estimates of heterogeneous treatment effects. However, forest algorithms are also well-known for their black-box nature, and therefore, do not characterize how input variables are involved in treatment effect ...
Bénard Clément, Josse Julie
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On the asymptotic covariance of the multivariate empirical copula process
Dependence Modeling, 2019 Genest and Segers (2010) gave conditions under which the empirical copula process associated with a random sample from a bivariate continuous distribution has a smaller asymptotic covariance than the standard empirical process based on a random sample ...
Genest Christian +2 more
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Treatment effect estimation with observational network data using machine learning
Journal of Causal InferenceCausal inference methods for treatment effect estimation usually assume independent units. However, this assumption is often questionable because units may interact, resulting in spillover effects between them.
Emmenegger Corinne +3 more
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Dependence Modeling, 2019 We study nonparametric estimators of conditional Kendall’s tau, a measure of concordance between two random variables given some covariates. We prove non-asymptotic pointwise and uniform bounds, that hold with high probabilities.
Derumigny Alexis, Fermanian Jean-David
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Demonstratio MathematicaThe aim of this article is to study a semi-functional partial linear regression model (SFPLR) for spatial data with responses missing at random (MAR).
Benchikh Tawfik +3 more
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