Results 31 to 40 of about 269 (169)

An optimal transport approach to estimating causal effects via nonlinear difference-in-differences

Journal of Causal Inference
We propose a nonlinear difference-in-differences (DiD) method to estimate multivariate counterfactual distributions in classical treatment and control study designs with observational data.
Torous William, Gunsilius Florian, Rigollet Philippe   +2 more
doaj   +1 more source

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, Deme El Hadji, Diop Aliou, Girard Stéphane   +3 more
doaj   +1 more source

Asymptotic normality of the relative error regression function estimator for censored and time series data

Dependence Modeling, 2021
Consider a survival time study, where a sequence of possibly censored failure times is observed with d-dimensional covariate The main goal of this article is to establish the asymptotic normality of the kernel estimator of the relative error regression ...
Bouhadjera Feriel, Saïd Elias Ould
doaj   +1 more source

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
doaj   +1 more source

All models are wrong, but which are useful? Comparing parametric and nonparametric estimation of causal effects in finite samples

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., Williams Nicholas T., Miles Caleb H., Antonelli Joseph, Diaz Ivan   +4 more
doaj   +1 more source

Projection Estimates of Constrained Functional Parameters [PDF]

, 2005
AMS classifications: 62G05; 62G07; 62G08; 62G20 ...
Segers, J., Guillou, A., Fils-Villetard, A.   +2 more
core   +1 more source

Independent component analysis by wavelets [PDF]

, 2009
ICA, Wavelets, Besov spaces, Non parametric density estimation, 62H12, 62G05,
Barbedor, Pascal, Pascal Barbedor
core   +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
doaj   +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, Prakasa Rao Indian, B. L. S. Prakasa Rao, Prakasa Rao, B. L. S.   +3 more
core   +1 more source

Fast estimation of Kendall's Tau and conditional Kendall's Tau matrices under structural assumptions

Dependence Modeling
Kendall’s tau and conditional Kendall’s tau matrices are multivariate (conditional) dependence measures between the components of a random vector. For large dimensions, available estimators are computationally expensive and can be improved by averaging ...
van der Spek Rutger, Derumigny Alexis
doaj   +1 more source