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Nonparametrics and Robust Methods
1987The chapters in this book have traced the origin and development of some of the major ideas and applications of statistics. A large part of this history has to do with inference about the mean of a distribution. In stating a confidence interval or testing a hypothesis about a mean based on sample data, the usual classical technique is to use the ...
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Robust nonparametric estimation with missing data
Journal of Statistical Planning and Inference, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Boente, Graciela +2 more
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Robust nonparametric estimation for spatial regression
Journal of Statistical Planning and Inference, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gheriballah, Abdelkader +2 more
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Robust Nonparametric Regression and Modality
2003The paper considers the problem of nonparametric regression with emphasis on controlling the number of local extremes and on resistance against patches of outliers. The robust taut string method is introduced and robustness properties are discussed. An automatic procedure is described.
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Robust Depth?Weighted Wavelet for Nonparametric Regression Models
Acta Mathematica Sinica, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Robust Nonparametric Copula Based Dependence Estimators
2011A fundamental problem in statistics is the estimation of dependence between random variables. While information theory provides standard measures of dependence (e.g. Shannon-, Renyi-, Tsallis-mutual information), it is still unknown how to estimate these quantities from i.i.d. samples in the most efficient way.
Poczos, Barnabas +4 more
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Robustness of Nonparametric Predictive Inference for Future Order Statistics
Journal of Statistical Theory and Practice, 2018Hana N. Alqifari, F. Coolen
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Robust and nonparametric subset selection procedures
Communications in Statistics - Theory and Methods, 1980Subset selection procedures based on ranks have been investigated by a number of authors previously. Their methods are based on ranking the samples from all the populations jointly. However, as was pointed out by Rizvi and Woodworth (1970), the procedures they proposed cannot control the probability of a correct selection over the entire parameter ...
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Bayesian Robustness and Bayesian Nonparametrics
2000Bayesian robustness studies the sensitivity of Bayesian answers to user inputs, especially to the specification of the prior. Nonparametric Bayesian models, on the other hand, refrain from specifying a specific prior functional form P, but instead assume a second-level hyperprior on P with support on a suitable space of probability measures ...
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