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Tolerance intervals in mixed models and confidence intervals for lognormal models
TLDR
In this article, the generalized confidence interval idea is used in the derivation of tolerance limits for the one-way random model, and confidence intervals for the mean of a lognormal model, using also an adaptation of the concept of generalized confidence intervals.Abstract:
The generalized confidence interval idea is used in the derivation of tolerance limits for the one-way random model. We also present confidence intervals for the mean of a lognormal model, using also an adaptation of the concept of generalized confidence interval. Simulation studies an real data examples are provided to illustrate the performance of both methods.read more
References
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Book ChapterDOI
Generalized Confidence Intervals
TL;DR: In this paper, a generalized version of the confidence interval is defined, and the generalized confidence interval can be applied to the problem of constructing confidence intervals for the difference in two exponential means and for variance components in mixed models.
Journal ArticleDOI
Exact Statistical Methods for Data Analysis
Journal ArticleDOI
Likelihood-based confidence intervals for a log-normal mean.
TL;DR: The modified signed log‐likelihood ratio method produces a confidence interval with a nearly exact coverage probability and highly accurate and symmetric error probabilities even for extremely small sample sizes.
Journal ArticleDOI
Tolerance intervals in a two-way nested model with mixed or random effects
TL;DR: In this paper, Krishnamoorthy and Mathew derived a one-sided tolerance interval in a two-way nested model with mixed or random effects using the generalized confidence interval idea.
Journal ArticleDOI
Inferences on the mean response in a log-regression model: the generalized variable approach.
Lili Tian,Jianrong Wu +1 more
TL;DR: Simulation studies demonstrate that the proposed confidence intervals have satisfying coverage probabilities and are an ideal candidate for making inferences about the mean response in a log‐regression model.
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