Results 231 to 240 of about 20,217 (261)

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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Bootstrapping the nonparametric ARCH regression model

Statistics & Probability Letters, 2014
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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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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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Nonparametric Bootstrap Tests: Some Applications

1992
In a series of papers Beran (1984, 1986, 1988) proposed bootstrap techniques for hypothesis testing. These tests are concerned with the following situation. Let {X 1, X 2,…, X n} be an i.i.d. sample of n random variables with distribution function F and the parameter θ(F) which is a real-valued functional statistic to be tested for H 0: θ(F) = θ 0.
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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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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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Bayesian Nonparametric Bootstrap Confidence Intervals

1985
Abstract : Let X sub 1,...,X sub n be a random sample from an unknown probability distribution P on the sample space X, and let theta = theta(P) be a parameter of interest. This paper gives a Bayesian botstrap method of obtaining Bayes estimates and Bayesian confidence limits for theta, using a (non- degenerate) Dirichlet process prior for P.
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