Results 11 to 20 of about 2,562,360 (318)
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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Robustness of Some Nonparametric Procedures in Linear Models
The Annals of Mathematical Statistics, 1968 For the random variables $X_{ij}(i = 1, \cdots, N; j = 1, \cdots, r)$ consider the linear model \begin{equation*}\tag{1.1} X_{ij} = \mu + \beta_i + \tau_j + Y_{ij} (\sum \beta_i = 0, \sum\tau_j = 0),\end{equation*} where the $\tau$'s are treatment effects, the $\beta$'s are nuisance parameters (block effects), and the $Y_{ij}$'s are error components ...
P. Sen
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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
semanticscholar +1 more source
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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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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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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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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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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