Results 221 to 230 of about 35,984 (255)

Parametric Bootstrapping for Assessing Software Reliability Measures

2011 IEEE 17th Pacific Rim International Symposium on Dependable Computing, 2011
The bootstrapping is a statistical technique to replicate the underlying data based on the resampling, and enables us to investigate the statistical properties. It is useful to estimate standard errors and confidence intervals for complex estimators of complex parameters of the probability distribution from a small number of data.
Toshio Kaneishi, Tadashi Dohi
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Non-parametric bootstrap tests for parametric distribution families

Acta Scientiarum Mathematicarum, 2011
This paper considers the test on whether the underlying distribution of an independent and identically distributed sample is a specific member of parametric distribution family. A functional of the difference between the empirical distribution of the sample and estimated parametric distribution can be used as the test statistic but its critical value ...
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Bootstrap confidence intervals for a class of parametric problems

Biometrika, 1985
The paper considers an application of the bootstrap method to obtain confidence intervals for \(\theta =t(\eta)\) on the basis of data y coming from a multivariate normal with mean \(\eta\) and variance-covariance matrix identity. The usual approximate solution based on the MLE \({\hat \theta}=t({\hat \eta})\) using asymptotic variance \({\hat \sigma}\)
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Portfolio management with semi-parametric bootstrapping

Journal of Risk Management in Financial Institutions, 2010
Estimation risk is an important topic within the area of risk management. Uncertainties regarding parameter estimates carry on to the final statistical product, such as investment strategies, and need to be estimated and accounted for. Unless the exact expressions for the estimators’ variances are known, the product’s variability will be assessed ...
Beatriz Vaz De Melo Mendes, Ricardo Pereira Câmara Leal   +1 more
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Bootstrap non-parametric significance test

Journal of Nonparametric Statistics, 2007
In this paper, we consider the problem of testing the significance of covariates in a nonparametric regression model. We propose to use some bootstrap procedures to better approximate the finite sample distribution of the test statistics. We establish the asymptotic validity of the proposed bootstrap procedures.
Jingping Gu, Dingding Li, Dandan Liu
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Parametric bootstrap inference in bilinear models.

1999
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
LA ROCCA, Michele, VITALE, Cosimo Damiano   +1 more
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Parametric polyspectrum density estimation using the bootstrap method

Signal Processing, 2007
A method for obtaining the statistical distribution of parametric polyspectra when applied to a single set of short data record is presented. The method applies model-based bootstrap to a time-series data to obtain the statistical distribution of the coefficients of the approximating process of autoregressive moving average (ARMA) type with known order
Shahnoor Shanta, Visakan Kadirkamanathan
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Non‐parametric bootstrapping of partitioned datasets

TAXON, 2009
Abstract Non‐parametric bootstrapping is one of the most commonly used methods for branch support assessment. Unlike Bayesian posterior probability values, which are influenced by a priori data partitioning, non‐parametric bootstrapping is usually applied to unpartitioned ...
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Parametric Bootstrap for Fixed Edge-Probability Network Models

This paper studies parametric bootstrap methods for network data, with the goal of quantifying the uncertainty of network statistics of interest. While existing network resampling methods primarily focus on count statistics under node-exchangeable graphon models, we consider more general network statistics, including local statistics, under the Chung ...
Shao, Zhixuan, Le, Can M.
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Non-parametric and unsupervised Bayesian classification with Bootstrap sampling

Image and Vision Computing, 2004
Abstract In this paper, we propose a non-parametric and unsupervised Bayesian classification based on the principle of Bootstrap sampling (BS) which reduces the dependence effect of pixels in real images, and reduces the classification time. Given an original image, we randomly select a small representative set of pixels.
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