We review and contrast frequentist and Bayesian definitions of tolerance regions. We give conditions under which for large samples a Bayesian region also has frequentist validity, and study the latter for smaller samples in a simulation study. We discuss a computational strategy for computing a Bayesian two-sided tolerance interval for a Gaussian future variable, and apply this to the case of possibly unbalanced linear mixed models. We illustrate the method on a quality control experiment from the pharmaceutical industry.Comment: 26 pages, 2 figure
We present a new natural way to construct nonparametric multivariate tolerance regions. Unlike the c...
Closed form expressions for the likelihood and the predictive density under the Generalized Linear M...
This article considers one side hypothesis testing on the unknown value of the explanatory variable ...
A review on statistical tolerance intervals shows that the derivation of two-sided tolerance interva...
By using air-lead data analysed by Krishnamoorthy and Mathew (2009) a Bayesian procedure is applied ...
Tolerance intervals in a regression setting allow the user to quantify, with a specified degree of c...
Quality improvement efforts have become the cornerstone of all manufacturing processes. Quality can ...
A tolerance interval is a statistical interval that covers at least 100ρ% of the population of inter...
In applications of linear mixed-effects models, experimenters often desire uncertainty quantificatio...
A tolerance region for a population is a region computed using a random sample, so that the region w...
AbstractA tolerance region is a map from the sample space of one statistical model to the event spac...
Statistical tolerance limits are estimates of large (or small) quantiles of a distribution, quantiti...
Linear mixed models (LMMs) are suitable for clustered data and are common in biometrics, medicine, s...
Statistical tolerance interval is another type of interval estimator used for making statistical in...
Maximum likelihood estimation in logistic regression with mixed effects is known to often result in ...
We present a new natural way to construct nonparametric multivariate tolerance regions. Unlike the c...
Closed form expressions for the likelihood and the predictive density under the Generalized Linear M...
This article considers one side hypothesis testing on the unknown value of the explanatory variable ...
A review on statistical tolerance intervals shows that the derivation of two-sided tolerance interva...
By using air-lead data analysed by Krishnamoorthy and Mathew (2009) a Bayesian procedure is applied ...
Tolerance intervals in a regression setting allow the user to quantify, with a specified degree of c...
Quality improvement efforts have become the cornerstone of all manufacturing processes. Quality can ...
A tolerance interval is a statistical interval that covers at least 100ρ% of the population of inter...
In applications of linear mixed-effects models, experimenters often desire uncertainty quantificatio...
A tolerance region for a population is a region computed using a random sample, so that the region w...
AbstractA tolerance region is a map from the sample space of one statistical model to the event spac...
Statistical tolerance limits are estimates of large (or small) quantiles of a distribution, quantiti...
Linear mixed models (LMMs) are suitable for clustered data and are common in biometrics, medicine, s...
Statistical tolerance interval is another type of interval estimator used for making statistical in...
Maximum likelihood estimation in logistic regression with mixed effects is known to often result in ...
We present a new natural way to construct nonparametric multivariate tolerance regions. Unlike the c...
Closed form expressions for the likelihood and the predictive density under the Generalized Linear M...
This article considers one side hypothesis testing on the unknown value of the explanatory variable ...