Bayesian simultaneous prediction intervals and bounds for a finite population

Hamada, Michael Scott; Weaver, Brian Phillip

doi:10.1080/08982112.2019.1656811

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:

Hamada, Michael Scott ^[1];

^[1]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Publication Date:: 2019-12-17

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.  doi:10.1080/08982112.2019.1656811.

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                    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

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                    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.

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@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}

}

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Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

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https://doi.org/10.1080/08982112.2019.1656811

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Works referenced in this record:

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

Hamada, Michael; Johnson, Valen; Moore, Leslie M.
Technometrics, Vol. 46, Issue 4
DOI: 10.1198/004017004000000518

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

Weaver, B. P.; Hamada, M. S.
Quality Engineering, Vol. 28, Issue 4
DOI: 10.1080/08982112.2016.1167220

Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)
journal, September 2006

Gelman, Andrew
Bayesian Analysis, Vol. 1, Issue 3
DOI: 10.1214/06-BA117A

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

Abstract

Citation Formats

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

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

Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper) journal, September 2006

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

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

Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)
journal, September 2006