Results 21 to 30 of about 252 (167)
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
doaj +1 more source
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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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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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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Minimally capturing heterogeneous complier effect of endogenous treatment for any outcome variable
Journal of Causal Inference, 2023 When a binary treatment DD is possibly endogenous, a binary instrument δ\delta is often used to identify the “effect on compliers.” If covariates XX affect both DD and an outcome YY, XX should be controlled to identify the “XX-conditional complier ...
Lee Goeun +2 more
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Targeted maximum likelihood based estimation for longitudinal mediation analysis
Journal of Causal InferenceCausal mediation analysis with random interventions has become an area of significant interest for understanding time-varying effects with longitudinal and survival outcomes.
Wang Zeyi +5 more
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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
wiley +1 more source
Cramer-Rao type integral inequalities for general loss functions
, 2001 Bayes risk, Cramer-Rao type integral inequality, Hajek-LeCam lower bound, locally asymptotic minimax error, lower bound, 62G05,
B. Prakasa Rao +3 more
core +1 more source
Nonparametric C- and D-vine-based quantile regression
Dependence Modeling, 2022 Quantile regression is a field with steadily growing importance in statistical modeling. It is a complementary method to linear regression, since computing a range of conditional quantile functions provides more accurate modeling of the stochastic ...
Tepegjozova Marija +3 more
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