Kalimuthu Krishnamoorthy & Thomas Mathew 
Statistical Tolerance Regions [PDF ebook] 
Theory, Applications, and Computation

A modern and comprehensive treatment of tolerance intervals and
regions

The topic of tolerance intervals and tolerance regions has
undergone significant growth during recent years, with applications
arising in various areas such as quality control, industry, and
environmental monitoring. Statistical Tolerance Regions
presents the theoretical development of tolerance intervals and
tolerance regions through computational algorithms and the
illustration of numerous practical uses and examples. This is the
first book of its kind to successfully balance theory and practice,
providing a state-of-the-art treatment on tolerance intervals and
tolerance regions.

The book begins with the key definitions, concepts, and
technical results that are essential for deriving tolerance
intervals and tolerance regions. Subsequent chapters provide
in-depth coverage of key topics including:

* Univariate normal distribution

* Non-normal distributions

* Univariate linear regression models

* Nonparametric tolerance intervals

* The one-way random model with balanced data

* The multivariate normal distribution

* The one-way random model with unbalanced data

* The multivariate linear regression model

* General mixed models

* Bayesian tolerance intervals

A final chapter contains coverage of miscellaneous topics
including tolerance limits for a ratio of normal random variables,
sample size determination, reference limits and coverage intervals,
tolerance intervals for binomial and Poisson distributions, and
tolerance intervals based on censored samples. Theoretical
explanations are accompanied by computational algorithms that can
be easily replicated by readers, and each chapter contains exercise
sets for reinforcement of the presented material. Detailed
appendices provide additional data sets and extensive tables of
univariate and multivariate tolerance factors.

Statistical Tolerance Regions is an ideal book for
courses on tolerance intervals at the graduate level. It is also a
valuable reference and resource for applied statisticians,
researchers, and practitioners in industry and pharmaceutical
companies.

Tabella dei contenuti

List of Tables.

Preface.

1 Preliminaries.

1.1 Introduction.

1.2 Some Technical Results.

1.3 The Modified Large Sample (MLS) Procedure.

1.4 The Generalized P-value and Generalized Confidence Interval.

1.5 Exercises.

2 Univariate Normal Distribution.

2.1 Introduction.

2.2 One-Sided Tolerance Limits for a Normal Population.

2.3 Two-Sided Tolerance Intervals.

2.4 Tolerance Limits for X1 – X2.

2.5 Simultaneous Tolerance Limits for Normal Populations.

2.6 Exercises.

3 Univariate Linear Regression Model.

3.1 Notations and Preliminaries.

3.2 One-Sided Tolerance Intervals and Simultaneous Tolerance Intervals.

3.3 Two-sided Tolerance Intervals and Simultaneous Tolerance Intervals.

3.4 The Calibration Problem.

3.5 Exercises.

4 The One-Way Random Model with Balanced Data.

4.1 Notations and Preliminaries.

4.2 Two Examples.

4.3 One-Sided Tolerance Limits for N(µ, sigma²tau + sigma²taue).

4.4 One-Sided Tolerance Limits for N(µ, sigma²tau¨).

4.5 Two-Sided Tolerance Intervals for N(µ, sigma²tau + sigma²taue).

4.6 Two-Sided Tolerance Intervals for N(µ, sigma²tau¨).

4.7 Exercises.

5 The One-Way Random Model with Unbalanced Data.

5.1 Notations and Preliminaries.

5.2 Two Examples.

5.3 One-Sided Tolerance Limits for N(µ, sigma²tau + sigma²e).

5.4 One-Sided Tolerance Limits for N(µ, sigma²tau).

5.5 Two-Sided Tolerance Intervals.

5.6 Exercises.

6 Some General Mixed Models.

6.1 Notations and Preliminaries.

6.2 Some Examples.

6.3 Tolerance Intervals in a General Setting.

6.4 A General Model with Two Variance Components.

6.5 A One-Way Random Model with Covariates and Unequal Variances.

6.5 Testing Individual Bioequivalence.

6.6 Exercises.

7 Some Non-Normal Distributions.

7.1 Introduction.

7.2 Lognormal Distribution.

7.3 Gamma Distribution.

7.4 Two-Parameter Exponential Distribution.

7.5 Weibull Distribution.

7.6 Exercises.

8 Nonparametric Tolerance Intervals.

8.1 Notations and Preliminaries.

8.2 Order Statistics and Their Distributions.

8.3 One-Sided Tolerance Limits and Exceedance Probabilities.

8.4 Tolerance Intervals.

8.5 Confidence Intervals for Population Quantiles.

8.6 Sample Size Calculation.

8.7 Nonparametric Multivariate Tolerance Regions.

8.8 Exercises.

9 The Multivariate Normal Distribution.

9.1 Introduction.

9.2 Notations and Preliminaries.

9.3 Some Approximate Tolerance Factors.

9.4 Methods Based on Monte Carlo Simulation.

9.5 Simultaneous Tolerance Intervals.

9.6 Tolerance Regions for Some Special Cases.

9.7 Exercises.

10 The Multivariate Linear Regression Model.

10.1 Preliminaries.

10.2 Approximations for the Tolerance Factor.

10.3 Accuracy of the Approximate Tolerance Factors.

10.4 Methods Based on Monte Carlo Simulation.

10.5 Application to the Example.

10.6 Multivariate Calibration.

10.7 Exercises.

11 Bayesian Tolerance Intervals.

11.1 Notations and Preliminaries.

11.2 The Univariate Normal Distribution.

11.3 The One-Way Random Model With Balanced Data.

11.4 Two Examples.

11.5 Exercises.

12 Miscellaneous Topics.

12.1 Introduction.

12.2 beta-Expectation Tolerance Regions.

12.3 Tolerance Limits for a Ratio of Normal Random Variables.

12.4 Sample Size Determination.

12.5 Reference Limits and Coverage Intervals.

12.6 Tolerance Intervals for Binomial and Poisson Distributions.

12.7 Tolerance Intervals Based on Censored Samples.

12.8 Exercises.

Appendix A: Data Sets.

Appendix B: Tables.

References.

Index.

Circa l’autore

K. Krishnamoorthy, Ph D, is Professor in the Department of
Mathematics at the University of Louisiana at Lafayette. He is
Associate Editor of Communications in Statistics and has published
numerous journal articles in his areas of research interest, which
include tolerance regions, multivariate analysis, and statistical
computing.

Thomas Mathew, Ph D, is Professor in the Department of
Mathematics and Statistics at the University of Maryland, Baltimore
County. He currently focuses his research on tolerance regions,
inference in linear mixed and random models, and bioequivalence
testing. A Fellow of the Institute of Mathematical Statistics and
the American Statistical Association, Dr. Mathew is the coauthor of
Statistical Tests for Mixed Linear Models, also published by
Wiley.