Results 271 to 280 of about 20,763 (296)

Nonparametric Markov chain bootstrap for multiple imputation

Computational Statistics & Data Analysis, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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A Bootstrap Approach to Nonparametric Regression for Right Censored Data

Annals of the Institute of Statistical Mathematics, 2001
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Li, Gang, Datta, Somnath
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Nonparametric Assessment of Toxicologic Assay Linearity by Bootstrap Analysis

Journal of Analytical Toxicology, 1992
An 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, D G, Wilkins, D L, Loughmiller, D E, Rollins   +3 more
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A nonparametric approach for spectrum sensing using bootstrap techniques

2014 IEEE Global Communications Conference, 2014
This 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, Pei-Jung Chung, John S. Thompson   +2 more
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Bootstrapping the nonparametric ARCH regression model

Statistics & Probability Letters, 2014
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Nonparametric distributed detection using bootstrapping and fisher's method

2018 52nd Annual Conference on Information Sciences and Systems (CISS), 2018
This 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, Poor, H. Vincent, Halme, Topi   +3 more
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A Nonparametric bootstrapped estimate of the change-point

Journal of Nonparametric Statistics, 1993
A 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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Bootstrap confidence bands in nonparametric regression

1994
In the present paper we construct asymptotic confidence bands in nonparametric regression. Our assumptions admit unequal variances of the observations and nonuniform, possibly considerably clustered design. The confidence band is based on an undersmoothed local linear estimator, and an appropriate quantile is obtained via the wild bootstrap made ...
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Nonparametric Bootstrap Estimation for Implicitly Weighted Robust Regression. [PDF]

, 2017
Implicitly weighted robust regression estimators for linear and nonlinear regression models include linear and nonlinear versions of the least trimmed squares and least weighted squares. After recalling known facts about these estimators, a nonparametric bootstrap procedure is proposed in this paper for estimates of their variances.
Kalina, J. (Jan), Peštová, B. (Barbora)   +1 more
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A NONPARAMETRIC HYPOTHESIS TEST VIA THE BOOTSTRAP RESAMPLING [PDF]

, 2001
This paper adapts an already existing nonparametric hypothesis test to the bootstrap framework. The test utilizes the nonparametric kernel regression method to estimate a measure of distance between the models stated under the null hypothesis. The bootstraped version of the test allows to approximate errors involved in the asymptotic hypothesis test.
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