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Statistical Tolerance Regions is an ideal book for courses on tolerance intervals at the graduate level. K. Krishnamoorthy, PhD, is Professor in the Department of Mathematics at the University of Louisiana at Lafayette.
Statistical Tolerance Regions is an ideal book for courses on tolerance intervals at the graduate level. Thomas Mathew, PhD, is Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County.
From the Inside Flap. Statistical Tolerance Regions. Theory, Applications, and Computation. There are also several books on engineering statistics that include chapters on tolerance regions or intervals including the book by Bowker and Lieberman. What I find treated here that is not well covered in earlier texts are (1) multivariate models, (2) non-normal parametric models, (3) special bivariate models, (4) Bayesian methods, (5) tolerance intervals for censored data, (6) survival models, (7) mixed linear models and (8) multivariate calibration.
Kalimuthu Krishnamoorthy, Thomas Mathew - Statistical Tolerance Regions: Theory, Applications, and Computation (Wiley Series in Probability and Statistics). Kalimuthu Krishnamoorthy, Thomas Mathew. Читать pdf. Mathew Kuefler - The Manly Eunuch: Masculinity, Gender Ambiguity, and Christian Ideology in Late Antiquity.
Kalimuthu Krishnamoorthy, Thomas Mathew
Kalimuthu Krishnamoorthy, Thomas Mathew. 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.
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Statistical Tolerance Regions: Theory, Applications, and Computation (Wiley Series in Probability and Statistics).
Statistical Tolerance Regions book. Joe Hightower marked it as to-read Dec 14, 2017.
Statistical Tolerance Regions: Theory, Applications, and Computation. We consider the problem of deriving one-sided tolerance intervals in the one-way random model with balanced as well as unbalanced data, under the usual normality assumptions. Kavassery Mahadevan Krishnamoorthy, Thomas Mathew. K Krishnamoorthy, T Mathew. Journal of statistical planning and inference 115 (1), 103-121, 2003. John Wiley & Sons, 2009. Inferences on the means of lognormal distributions using generalized p-values and generalized confidence intervals. A parametric bootstrap approach for ANOVA with unequal variances: Fixed and random models. K Krishnamoorthy, F Lu, T Mathew. Computational Statistics & Data Analysis 51 (12), 5731-5742, 2007.
The topic of tolerance intervals and tolerance regions hasundergone significant growth during recent years, with applicationsarising in various areas such as quality control, industry, andenvironmental monitoring. Statistical Tolerance Regionspresents the theoretical development of tolerance intervals andtolerance regions through computational algorithms and theillustration of numerous practical uses and examples. This is thefirst book of its kind to successfully balance theory and practice,providing a state-of-the-art treatment on tolerance intervals andtolerance regions.
The book begins with the key definitions, concepts, andtechnical results that are essential for deriving toleranceintervals and tolerance regions. Subsequent chapters providein-depth coverage of key topics including:Univariate normal distributionNon-normal distributionsUnivariate linear regression modelsNonparametric tolerance intervalsThe one-way random model with balanced dataThe multivariate normal distributionThe one-way random model with unbalanced dataThe multivariate linear regression modelGeneral mixed modelsBayesian tolerance intervals
A final chapter contains coverage of miscellaneous topicsincluding tolerance limits for a ratio of normal random variables,sample size determination, reference limits and coverage intervals,tolerance intervals for binomial and Poisson distributions, andtolerance intervals based on censored samples. Theoreticalexplanations are accompanied by computational algorithms that canbe easily replicated by readers, and each chapter contains exercisesets for reinforcement of the presented material. Detailedappendices provide additional data sets and extensive tables ofunivariate and multivariate tolerance factors.
Statistical Tolerance Regions is an ideal book forcourses on tolerance intervals at the graduate level. It is also avaluable reference and resource for applied statisticians,researchers, and practitioners in industry and pharmaceuticalcompanies.