Results 231 to 240 of about 22,159 (266)
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A wild bootstrap approach for nonparametric repeated measurements
Computational Statistics & Data Analysis, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sarah Friedrich +2 more
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Parametric and nonparametric bootstrap methods for meta-analysis
Behavior Research Methods, 2005In a meta-analysis, the unknown parameters are often estimated using maximum likelihood, and inferences are based on asymptotic theory. It is assumed that, conditional on study characteristics included in the model, the between-study distribution and the sampling distributions of the effect sizes are normal.
Wim, Van Den Noortgate, Patrick, Onghena
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Bootstrap Initialization of Nonparametric Texture Models for Tracking
2000In bootstrap initialization for tracking, we exploit a weak prior model used to track a target to learn a stronger model, without manual intervention. We define a general formulation of this problem and present a simple taxonomy of such tasks. The formulation is instantiated with algorithms for bootstrap initialization in two domains: In one, the ...
Kentaro Toyama, Ying Wu 0001
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Nonparametric Markov chain bootstrap for multiple imputation
Computational Statistics & Data Analysis, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Nonparametric Assessment of Toxicologic Assay Linearity by Bootstrap Analysis
Journal of Analytical Toxicology, 1992An important aspect in the evaluation of toxicologic assay methodology is the assessment of calibration. This paper presents a new approach for validating calibration using bootstrap analysis. The technique is illustrated with a quantitative assay for benzoylecgonine in urine by gas chromatography/mass spectrometry (GC/MS).
G C, Critchfield +3 more
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A Nonparametric bootstrapped estimate of the change-point
Journal of Nonparametric Statistics, 1993A bootstrap resampling scheme is applied to a nonparametric estimator of the change-point in a sequence of independent observations. The nonparametric estimator is based on the Kolmogorov-Smirnov norm as proposed by Carlstein (1988). The consistency of the bootstrapped estimator along with the rate of convergence are provided.
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A nonparametric approach for spectrum sensing using bootstrap techniques
2014 IEEE Global Communications Conference, 2014This paper deals with the blind spectrum sensing problem for arbitrary noise. The majority of current methods consider the Gaussian noise. However, this assumption cannot model the impulsive noise due to the artificial source. In this paper, we remove the requirement on Gaussianity and propose a detection method based on the bootstrap technique.
Qi Huang +2 more
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Nonparametric distributed detection using bootstrapping and fisher's method
2018 52nd Annual Conference on Information Sciences and Systems (CISS), 2018This paper addresses the problem of distributed decision making when there is no or very vague knowledge about the probability models associated with the hypotheses. Such scenarios occur for example in the Internet of Things (IoT), data analytics, radio spectrum monitoring, sensor networks, environmental surveillance.
Koivunen, Visa +3 more
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Bootstrap Estimation for Nonparametric Efficiency Estimates [PDF]
, 1995 This paper develops a consistent bootstrap estimation procedure to obtain confidence intervals for nonparametric measures of productive efficiency. Although the methodology is illustrated in terms of technical efficiency measured by output distance functions, the technique can be easily extented to consistent non parametric frontier models.
WILSON, Paul, SIMAR, Leopold
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Bootstrapping the nonparametric ARCH regression model
Statistics & Probability Letters, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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