Results 21 to 30 of about 22,874 (259)
Energies, 2021 Previous studies have demonstrated that non-parametric hedging models using temperature derivatives are highly effective in hedging profit/loss fluctuation risks for electric utilities.
Takuji Matsumoto, Yuji Yamada
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Robust Nonparametric Inference
Annual Review of Statistics and Its Application, 2018 In this article, we provide a personal review of the literature on nonparametric and robust tools in the standard univariate and multivariate location and scatter, as well as linear regression problems, with a special focus on sign and rank methods, their equivariance and invariance properties, and their robustness and efficiency.
Klaus Nordhausen, Hannu Oja
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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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Mortality Forecasting: How Far Back Should We Look in Time?
Risks, 2019 Extrapolative methods are one of the most commonly-adopted forecasting approaches in the literature on projecting future mortality rates. It can be argued that there are two types of mortality models using this approach.
Han Li, Colin O’Hare
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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
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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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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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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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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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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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