Results 231 to 240 of about 22,874 (259)

ROBUST NONPARAMETRIC SIMPLIFICATION OF POLYGONAL CHAINS

International Journal of Computational Geometry & Applications, 2013
In this paper we present a novel nonparametric method for simplifying piecewise linear curves and we apply this method as a statistical approximation of structure within sequential data in the plane. Specifically, given a sequence P of n points in the plane that determine a simple polygonal chain consisting of n−1 segments, we describe algorithms for ...
Stephane Durocher, Alexandre Leblanc, Jason Morrison, Matthew Skala   +3 more
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On a Nonparametric Robust Method of Detection of Signals

Journal of Mathematical Sciences, 2002
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Shevlyakov, G. L., Vilchevskij, N. O., Khvatova, T. Yu.   +2 more
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Robust nonparametric estimation for functional data

Journal of Nonparametric Statistics, 2008
Robust estimation provides an alternative approach to classical methods, for instance, when the data are affected by the presence of outliers.
Christophe Crambes, Laurent Delsol, Ali Laksaci   +2 more
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Nonparametric and robust methods in econometrics

Journal of Econometrics, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Luiz Renato Lima, Marcelo Moreira, Jack Porter, Zhijie Xiao   +3 more
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Nonparametrics and Robust Methods

1987
The 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 Bayesian Nonparametric Regression

1996
We discuss a Bayesian approach to nonparametric regression which is robust against outliers and discontinuities in the underlying function. Our approach uses Markov chain Monte Carlo methods to perform a Bayesian analysis of conditionally Gaussian state space models.
C. K. Carter, R. Kohn
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A robust nonparametric method for quantifying undetected extinctions

Conservation Biology, 2016
Abstract How many species have gone extinct in modern times before being described by science? To answer this question, and thereby get a full assessment of humanity's impact on biodiversity, statistical methods that quantify undetected extinctions are required.
Chisholm, R., Giam, X., Sadanandan, K., Fung, T., Rheindt, F.   +4 more
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NONPARAMETRIC LIKELIHOOD: EFFICIENCY AND ROBUSTNESS

The Japanese Economic Review, 2007
Nonparametric likelihood is a natural generalization of parametric likelihood and it offers effective methods for analysing economic models with nonparametric components. This is of great interest, since econometric theory rarely suggests a parametric form of the probability law of data.
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Robust Statistics for Nonparametric Group Analysis in fMRI

3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006., 2006
In order to deal with inhomogeneous groups of subjects in fMRI studies, we investigate several robust statistics to perform random-effect analysis on the mean population effect (sign statistic, Wilcoxon's signed rank statistic and empirical t statistic).
Sébastien Mériaux, Alexis Roche, Bertrand Thirion, Ghislaine Dehaene-Lambertz   +3 more
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Nonparametric Bayesian robustness [PDF]

, 2010
A new, nonparametric, approach to Bayesian robustness is presented. Whereas many studies in Bayesian robustness have dealt with a parametric sampling distribution, considering classes of prior distributions on the parameters, here we assume that the sampling distribution comes from a Dirichlet process with a parameter = , with > 0 and being a ...
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