Results 1 to 10 of about 110 (98)
Complete consistency for the estimator of nonparametric regression model based on m-END errors
Open Mathematics, 2021 In this paper, we study the complete consistency for the estimator of nonparametric regression model based on m-END errors and obtain the convergence rates of the complete consistency under more general conditions.
Zhang Shui-Li, Hou Tiantian, Qu Cong
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A fundamental measure of treatment effect heterogeneity
Journal of Causal Inference, 2021 The stratum-specific treatment effect function is a random variable giving the average treatment effect (ATE) for a randomly drawn stratum of potential confounders a clinician may use to assign treatment.
Levy Jonathan +3 more
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Identification of causal intervention effects under contagion
Journal of Causal Inference, 2021 Defining and identifying causal intervention effects for transmissible infectious disease outcomes is challenging because a treatment – such as a vaccine – given to one individual may affect the infection outcomes of others. Epidemiologists have proposed
Cai Xiaoxuan +2 more
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Explaining predictive models using Shapley values and non-parametric vine copulas
Dependence Modeling, 2021 In this paper the goal is to explain predictions from complex machine learning models. One method that has become very popular during the last few years is Shapley values.
Aas Kjersti +3 more
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Open Mathematics, 2022 In this article, we present a new robust estimation procedure based on the exponential squared loss function for varying coefficient partially functional linear regression models, where the slope function and nonparametric coefficients are approximated ...
Sun Jun, Liu Wanrong
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Nonparametric inference for interventional effects with multiple mediators
Journal of Causal Inference, 2021 Understanding the pathways whereby an intervention has an effect on an outcome is a common scientific goal. A rich body of literature provides various decompositions of the total intervention effect into pathway-specific effects.
Benkeser David, Ran Jialu
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Incremental intervention effects in studies with dropout and many timepoints#
Journal of Causal Inference, 2021 Modern longitudinal studies collect feature data at many timepoints, often of the same order of sample size. Such studies are typically affected by dropout and positivity violations.
Kim Kwangho +2 more
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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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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
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International Journal of Stochastic Analysis, Volume 15, Issue 3, Page 207-219, 2002., 2002 In this paper we are concerned with the heteroscedastic regression model yi = xiβ + g(ti) + σiei, 1 ≤ i ≤ n under correlated errors ei, where it is assumed that σi2=f(ui), the design points (xi, ti, ui) are known and nonrandom, and g and f are unknown functions. The interest lies in the slope parameter β.
Han-Ying Liang, Bing-Yi Jing
wiley +1 more source