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Parametric bootstrap inference in bilinear models.
1999zbMATH Open Web Interface contents unavailable due to conflicting licenses.
LA ROCCA, Michele +1 more
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Parametric polyspectrum density estimation using the bootstrap method
Signal Processing, 2007A 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, 2009Abstract 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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Optimal choice between parametric and non-parametric bootstrap estimates
Mathematical Proceedings of the Cambridge Philosophical Society, 1994AbstractA parametric bootstrap estimate (PB) may be more accurate than its non-parametric version (NB) if the parametric model upon which it is based is, at least approximately, correct. Construction of an optimal estimator based on both PB and NB is pursued with the aim of minimizing the mean squared error.
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Parametric bootstrap for reliability assessment
2010The article concerns the evaluation of reliability for stress-strength models. A parametric bootstrap approach is suggested for the interval estimation of reliability, and its performance is checked and compared to other procedures available from literature through a simulation study performed on artificial data.
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An approximate bootstrap technique for variance estimation in parametric images
Medical Image Analysis, 1998Parametric imaging procedures offer the possibility of comprehensive assessment of tissue metabolic activity. Estimating variances of these images is important for the development of inference tools in a diagnostic setting. However, these are not readily obtained because the complexity of the radio-tracer models used in the generation of a parametric ...
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Non-parametric and unsupervised Bayesian classification with Bootstrap sampling
Image and Vision Computing, 2004Abstract 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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A parametric version of jackknife-after-bootstrap
1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274), 2002openaire +1 more source