Results 11 to 20 of about 110 (98)
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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Journal of Causal Inference, 2023 There is a long-standing debate in the statistical, epidemiological, and econometric fields as to whether nonparametric estimation that uses machine learning in model fitting confers any meaningful advantage over simpler, parametric approaches in finite ...
Rudolph Kara E. +4 more
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Nonparametric density estimators based on nonstationary absolutely regular random sequences
International Journal of Stochastic Analysis, Volume 9, Issue 3, Page 233-254, 1996., 1995 In this paper, the central limit theorems for the density estimator and for the integrated square error are proved for the case when the underlying sequence of random variables is nonstationary. Applications to Markov processes and ARMA processes are provided.
Michel Harel, Madan L. Puri
wiley +1 more source
A new simulation estimator of system reliability
International Journal of Stochastic Analysis, Volume 7, Issue 3, Page 331-336, 1994., 1993 A basic identity is proven and applied to obtain new simulation estimators concerning (a) system reliability, (b) a multi‐valued system. We show that the variance of this new estimator is often of the order α2 when the usual raw estimator has variance of the order α and α is small.
Sheldon M. Ross
wiley +1 more source
A note on efficient minimum cost adjustment sets in causal graphical models
Journal of Causal Inference, 2022 We study the selection of adjustment sets for estimating the interventional mean under an individualized treatment rule. We assume a non-parametric causal graphical model with, possibly, hidden variables and at least one adjustment set composed of ...
Smucler Ezequiel, Rotnitzky Andrea
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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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Estimation of the tail-index in a conditional location-scale family of heavy-tailed distributions
Dependence Modeling, 2019 We introduce a location-scale model for conditional heavy-tailed distributions when the covariate is deterministic. First, nonparametric estimators of the location and scale functions are introduced.
Ahmad Aboubacrène Ag +3 more
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2D score-based estimation of heterogeneous treatment effects
Journal of Causal Inference, 2023 Statisticians show growing interest in estimating and analyzing heterogeneity in causal effects in observational studies. However, there usually exists a trade-off between accuracy and interpretability for developing a desirable estimator for treatment ...
Ye Steven Siwei +2 more
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About tests of the “simplifying” assumption for conditional copulas
Dependence Modeling, 2017 We discuss the so-called “simplifying assumption” of conditional copulas in a general framework. We introduce several tests of the latter assumption for non- and semiparametric copula models.
Derumigny Alexis, Fermanian Jean-David
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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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