Probability-of-coverage tolerance intervals for the beta binomial

Psychometrika ◽  
1986 ◽  
Vol 51 (1) ◽  
pp. 137-141
Author(s):  
David Jarjoura
Keyword(s):  
Tolerance Intervals ◽  
Probability Of Coverage

Tolerance Intervals for True Scores

1985 ◽  
Vol 10 (1) ◽  
pp. 1-17 ◽  
Author(s):  
David Jarjoura
Keyword(s):  
Confidence Intervals ◽  
Practical Implication ◽  
Educational Measurement ◽  
True Score ◽  
Tolerance Intervals ◽  
Observed Score ◽  
True Scores

Issues regarding tolerance and confidence intervals are discussed within the context of educational measurement and conceptual distinctions are drawn between these two types of intervals. Points are raised about the advantages of tolerance intervals when the focus is on a particular observed score rather than a particular examinee. Because tolerance intervals depend on strong true score models, a practical implication of the study is that true score tolerance intervals are fairly insensitive to differences in assumptions among the five models studied.

Two-sided Bayesian and frequentist tolerance intervals: general asymptotic results with applications

Statistics ◽  
2013 ◽  
Vol 48 (3) ◽  
pp. 524-538 ◽  
Author(s):  
Dharini Pathmanathan ◽  
Rahul Mukerjee ◽  
S. H. Ong
Keyword(s):  
Asymptotic Results ◽  
Tolerance Intervals

Calibration and Simultaneous Tolerance Intervals for Regression

Technometrics ◽  
1991 ◽  
Vol 33 (2) ◽  
pp. 211-219 ◽  
Author(s):  
Robert W. Mee ◽  
Keith R. Eberhardt ◽  
Charles P. Reeve
Keyword(s):  
Tolerance Intervals

scholarly journals Tolerance Intervals in a Heteroscedastic Linear Regression Context with Applications to Aerospace Equipment Surveillance

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Janet Myhre ◽  
Daniel R. Jeske ◽  
Michael Rennie ◽  
Yingtao Bi
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Performance Metrics ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Automated System ◽  
Least Squares Regression ◽  
Data Set ◽  
Tolerance Intervals ◽  
Power Of The Test

A heteroscedastic linear regression model is developed from plausible assumptions that describe the time evolution of performance metrics for equipment. The inherited motivation for the related weighted least squares analysis of the model is an essential and attractive selling point to engineers with interest in equipment surveillance methodologies. A simple test for the significance of the heteroscedasticity suggested by a data set is derived and a simulation study is used to evaluate the power of the test and compare it with several other applicable tests that were designed under different contexts. Tolerance intervals within the context of the model are derived, thus generalizing well-known tolerance intervals for ordinary least squares regression. Use of the model and its associated analyses is illustrated with an aerospace application where hundreds of electronic components are continuously monitored by an automated system that flags components that are suspected of unusual degradation patterns.

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