Results 11 to 20 of about 2,509,073 (310)
MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
Journal of Statistical Software, 2011 MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods.
Daniel Ho +3 more
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On robustness and local differential privacy [PDF]
Annals of Statistics, 2022 It is of soaring demand to develop statistical analysis tools that are robust against contamination as well as preserving individual data owners' privacy. In spite of the fact that both topics host a rich body of literature, to the best of our knowledge,
Mengchu Li, Thomas B. Berrett, Yi Yu
semanticscholar +1 more source
Marine Structures, 2021 Floating structures oscillate in waves, where these wave-induced motions may be critical for various marine operations. An important consideration is thereby given to the sea states at the planning and operating stages for an offshore project.
Z. Ren +4 more
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Robust Nonparametric Generators of Random Variables
Russian Physics Journal, 2023 A method of constructing consistent and effective algorithms for robust nonparametric generators of random variables is considered for statistical simulation problems and bootstrap procedures. Semiparametric and semi-nonparametric algorithms of generators have been synthesized for inhomogeneous experimental data.
Simakhin, V. A. +2 more
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Robust nonparametric regression: A review
WIREs Computational Statistics, 2019 AbstractNonparametric regression methods provide an alternative approach to parametric estimation that requires only weak identification assumptions and thus minimizes the risk of model misspecification. In this article, we survey some nonparametric regression techniques, with an emphasis on kernel‐based estimation, that are additionally robust to ...
Pavel Čížek, Serhan Sadıkoğlu
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Outliers vs Robustness in Nonparametric Methods of Regression
Acta Universitatis Lodziensis. Folia Oeconomica, 2018 The article addresses the question of how robust methods of regression are against outliers in a given data set. In the first part, we presented the selected methods used to detect outliers.
Joanna Trzęsiok
doaj +1 more source
How Do Financial Development and Renewable Energy Affect Consumption-Based Carbon Emissions?
Mathematical and Computational Applications, 2022 This paper bridges the gap in the literature by employing the novel quantile-on-quantile (QQ) approach, the quantile regression approach, and the nonparametric Granger causality test in quantiles to assess the effect of international trade on consumption-
Abraham Ayobamiji Awosusi +3 more
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Asymptotic equivalence and adaptive estimation for robust nonparametric regression [PDF]
, 2009 Asymptotic equivalence theory developed in the literature so far are only for bounded loss functions. This limits the potential applications of the theory because many commonly used loss functions in statistical inference are unbounded.
Cai, T. Tony, Zhou, Harrison H.
core +4 more sources
Comparison the Robust Estimators Nonparametric of Nonparametric Regressions
Tikrit Journal of Pure Science, 2023 In order to get rid of or reduce the abnormal values of some phenomena that may be the reason for not obtaining the desired results. This makes us to get conclusions far from reality for the phenomenon we are studying. That the traditional nonparametric estimators are very sensitive to anomalous values, which prompted us to use the fortified ...
Ali Fadhil Abduljabbar +1 more
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NeuroImage, 2018 &NA; Brain function arises from networks of distributed brain areas whose directed interactions vary at subsecond time scales. To investigate such interactions, functional directed connectivity methods based on nonparametric spectral factorization are ...
Mattia F. Pagnotta +2 more
semanticscholar +1 more source