Results 261 to 270 of about 607,854 (297)

Signal-Analytics Modeling of Fluorescence Time-to-Detection for <i>E. coli</i> in Treated Wastewater: A Joint Censoring and Sensor Model Approach. [PDF]

open access: yesACS Omega
Haab CA   +10 more
europepmc   +1 more source

Parametric and Nonparametric Bootstrap Methods for Meta-Analysis

Behavior Research Methods, 2005
In 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.
Patrick Onghena, Wim van den Noortgate
exaly   +3 more sources

A Parametric Bootstrap Approach for the Equality of Coefficients of Variation

Computational Statistics, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mohammad Reza Kazemi, Ali Akbar Jafari
exaly   +2 more sources

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.
exaly   +2 more sources

Introduction to the Non-Parametric Bootstrap

open access: yes, 2019
The bootstrap is a computation-intensive data resampling methodology for assessing the accuracy of a statistical estimator and for making inferences about unknown population parameters. This article provides an introduction to the ordinary non-parametric
Berrar, Daniel, Daniel Berrar
core   +3 more sources

On Parametric Bootstrapping and Bayesian Prediction

Scandinavian Journal of Statistics, 2004
Abstract.  We investigate bootstrapping and Bayesian methods for prediction. The observations and the variable being predicted are distributed according to different distributions. Many important problems can be formulated in this setting. This type of prediction problem appears when we deal with a Poisson process.
Fushiki, Tadayoshi   +2 more
openaire   +2 more sources

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