Results 1 to 10 of about 287 (69)
Robust analogs to the coefficient of variation [PDF]
Journal of Applied Statistics, 2022 Luke A Prendergast, Robert G Staudte
exaly +2 more sources
Causal effect on a target population: A sensitivity analysis to handle missing covariates
Journal of Causal Inference, 2022 Randomized controlled trials (RCTs) are often considered the gold standard for estimating causal effect, but they may lack external validity when the population eligible to the RCT is substantially different from the target population.
Colnet Bénédicte +3 more
doaj +1 more source
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
doaj +1 more source
Journal of Causal Inference, 2022 Analysts often use data-driven approaches to supplement their knowledge when selecting covariates for effect estimation. Multiple variable selection procedures for causal effect estimation have been devised in recent years, but additional developments ...
Talbot Denis, Beaudoin Claudia
doaj +1 more source
Journal of Causal Inference, 2022 Recent studies have indicated that it is possible to protect individuals from HIV infection using passive infusion of monoclonal antibodies. However, in order for monoclonal antibodies to confer robust protection, the antibodies must be capable of ...
Jin Yutong, Benkeser David
doaj +1 more source
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
doaj +1 more source
Two symmetric and computationally efficient Gini correlations
Dependence Modeling, 2020 Standard Gini correlation plays an important role in measuring the dependence between random variables with heavy-tailed distributions. It is based on the covariance between one variable and the rank of the other. Hence for each pair of random variables,
Vanderford Courtney +2 more
doaj +1 more source
Well-posed Bayesian inverse problems and heavy-tailed stable quasi-Banach space priors [PDF]
, 2016 This article extends the framework of Bayesian inverse problems in infinite-dimensional parameter spaces, as advocated by Stuart (Acta Numer. 19:451--559, 2010) and others, to the case of a heavy-tailed prior measure in the family of stable distributions,
Sullivan, T. J.
core +3 more sources
On the efficiency of Gini's mean difference [PDF]
, 2014 18 pages, 3 figures, 8 tables Acknowledgments We are indebted to Herold Dehling for introducing us to the theory of U-statistics, to Roland Fried for introducing us to robust statistics, and to Alexander Dürre, who has demonstrated the benefit of complex
Gerstenberger, Carina, Vogel, Daniel
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Adaptive goodness-of-fit tests based on signed ranks [PDF]
, 2008 Within the nonparametric regression model with unknown regression function $l$ and independent, symmetric errors, a new multiscale signed rank statistic is introduced and a conditional multiple test of the simple hypothesis $l=0$ against a nonparametric ...
Rohde, Angelika
core +4 more sources