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Title: Quality Quandaries: Predicting a Population of Curves

Abstract

We present a random effects spline regression model based on splines that provides an integrated approach for analyzing functional data, i.e., curves, when the shape of the curves is not parametrically specified. An analysis using this model is presented that makes inferences about a population of curves as well as features of the curves.

Authors:
ORCiD logo [1];  [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
OSTI Identifier:
1416317
Report Number(s):
LA-UR-17-30878
Journal ID: ISSN 0898-2112; TRN: US1800919
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Accepted Manuscript
Journal Name:
Quality Engineering
Additional Journal Information:
Journal Volume: 30; Journal Issue: 2; Journal ID: ISSN 0898-2112
Publisher:
American Society for Quality Control
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 42 ENGINEERING

Citation Formats

Fugate, Michael Lynn, Hamada, Michael Scott, and Weaver, Brian Phillip. Quality Quandaries: Predicting a Population of Curves. United States: N. p., 2017. Web. doi:10.1080/08982112.2017.1417601.
Fugate, Michael Lynn, Hamada, Michael Scott, & Weaver, Brian Phillip. Quality Quandaries: Predicting a Population of Curves. United States. doi:10.1080/08982112.2017.1417601.
Fugate, Michael Lynn, Hamada, Michael Scott, and Weaver, Brian Phillip. Tue . "Quality Quandaries: Predicting a Population of Curves". United States. doi:10.1080/08982112.2017.1417601. https://www.osti.gov/servlets/purl/1416317.
@article{osti_1416317,
title = {Quality Quandaries: Predicting a Population of Curves},
author = {Fugate, Michael Lynn and Hamada, Michael Scott and Weaver, Brian Phillip},
abstractNote = {We present a random effects spline regression model based on splines that provides an integrated approach for analyzing functional data, i.e., curves, when the shape of the curves is not parametrically specified. An analysis using this model is presented that makes inferences about a population of curves as well as features of the curves.},
doi = {10.1080/08982112.2017.1417601},
journal = {Quality Engineering},
number = 2,
volume = 30,
place = {United States},
year = {2017},
month = {12}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Figures / Tables:

Figure 1 Figure 1: Onionskin curves for nine parts.

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Works referenced in this record:

Analyzing degradation data with a random effects spline regression model
journal, March 2017


Domain-Level Covariance Analysis for Multilevel Survey Data With Structured Nonresponse
journal, December 2008

  • O’Malley, A. James; Zaslavsky, Alan M.
  • Journal of the American Statistical Association, Vol. 103, Issue 484
  • DOI: 10.1198/016214508000000724

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


Methods for Characterizing and Comparing Populations of Shock Wave Curves
journal, November 2013


    Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.