Results 21 to 30 of about 2,562,360 (318)
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
Counterfactual Sensitivity and Robustness [PDF]
Econometrica, 2019 We propose a framework for analyzing the sensitivity of counterfactuals to parametric assumptions about the distribution of latent variables in structural models.
T. Christensen, Benjamin Connault
semanticscholar +1 more source
Psych, 2023 In psychological research, comparisons between two groups are frequently made to demonstrate that one group exhibits higher values. Although Welch’s unequal variances t-test has become the preferred parametric test for this purpose, surpassing Student’s ...
Julian D. Karch
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Journal of Water and Climate Change, 2021 This study aims to investigate different statistical bias correction techniques to improve the output of a regional climate model (RCM) of daily rainfall for the upper Ping River Basin in Northern Thailand.
Srisunee Wuthiwongtyohtin
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On robust cross-validation for nonparametric smoothing [PDF]
Computational Statistics, 2012 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Oliver Morell, Dennis Otto, Roland Fried
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Generalized Wald-type Tests based on Minimum Density Power Divergence Estimators [PDF]
, 2015 In testing of hypothesis the robustness of the tests is an important concern. Generally, the maximum likelihood based tests are most efficient under standard regularity conditions, but they are highly non-robust even under small deviations from the ...
Basu, Ayanendranath +3 more
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Reference charts for fetal cerebellar vermis height: A prospective cross-sectional study of 10605 fetuses [PDF]
, 2016 A prospective cross-sectional study between September 2009 and December 2014 was carried out at ALTAMEDICA Fetal–Maternal Medical Centre, Rome, Italy. Of 25203 fetal biometric measurements, 12167 (48%) measurements of the cerebellar vermis were available.
Aloisi, Alessia +5 more
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Robust nonparametric regression by controlling sparsity* [PDF]
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011 Nonparametric methods are widely applicable to statistical learning problems, since they rely on a few modeling assumptions. In this context, the fresh look advocated here permeates benefits from variable selection and compressive sampling, to robustify nonparametric regression against outliers.
Gonzalo Mateos, Georgios B. Giannakis
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On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference [PDF]
Journal of the American Statistical Association, 2015 Nonparametric methods play a central role in modern empirical work. While they provide inference procedures that are more robust to parametric misspecification bias, they may be quite sensitive to tuning parameter choices.
Sebastian Calonico +2 more
semanticscholar +1 more source