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Title: Bayesian simultaneous prediction intervals and bounds for a finite population

Abstract

Simultaneous prediction intervals and bounds provide a statistical characterization of a proportion of a finite population. In this article we consider these predictions from a Bayesian inferential approach and different ways to evaluate them including simulation.

Authors:
 [1]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1597344
Report Number(s):
LA-UR-19-24501
Journal ID: ISSN 0898-2112
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Accepted Manuscript
Journal Name:
Quality Engineering
Additional Journal Information:
Journal Volume: 32; Journal Issue: 2; Journal ID: ISSN 0898-2112
Publisher:
American Society for Quality Control
Country of Publication:
United States
Language:
English
Subject:
01 COAL, LIGNITE, AND PEAT

Citation Formats

Hamada, Michael Scott, and Weaver, Brian Phillip. Bayesian simultaneous prediction intervals and bounds for a finite population. United States: N. p., 2019. Web. https://doi.org/10.1080/08982112.2019.1656811.
Hamada, Michael Scott, & Weaver, Brian Phillip. Bayesian simultaneous prediction intervals and bounds for a finite population. United States. https://doi.org/10.1080/08982112.2019.1656811
Hamada, Michael Scott, and Weaver, Brian Phillip. Tue . "Bayesian simultaneous prediction intervals and bounds for a finite population". United States. https://doi.org/10.1080/08982112.2019.1656811. https://www.osti.gov/servlets/purl/1597344.
@article{osti_1597344,
title = {Bayesian simultaneous prediction intervals and bounds for a finite population},
author = {Hamada, Michael Scott and Weaver, Brian Phillip},
abstractNote = {Simultaneous prediction intervals and bounds provide a statistical characterization of a proportion of a finite population. In this article we consider these predictions from a Bayesian inferential approach and different ways to evaluate them including simulation.},
doi = {10.1080/08982112.2019.1656811},
journal = {Quality Engineering},
number = 2,
volume = 32,
place = {United States},
year = {2019},
month = {12}
}

Journal Article:
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Works referenced in this record:

Bayesian Prediction Intervals and Their Relationship to Tolerance Intervals
journal, November 2004


Quality quandaries: A gentle introduction to Bayesian statistics
journal, June 2016