Modelling Variation in Industrial Experiments

DOI10.2307/2348091zbMath0825.62734OpenAlexW2474350117MaRDI QIDQ4844014

Jan Engel

Publication date: 17 August 1995

Published in: Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/2348091

zbMATH Keywords

Taguchi methodvariance functioncontrol factorsnoise factorsexperimental designextended quasi-likelihoodvariance modellingpseudonormal likelihood

Mathematics Subject Classification ID

Applications of statistics in engineering and industry; control charts (62P30)

Related Items (7)

Minimizing a general loss function in off-line quality control ⋮ Robustness properties of minimally-supported Bayesian D-optimal designs for heteroscedastic models ⋮ On variable selection in joint modeling of mean and dispersion ⋮ Generalized linear models for the analysis of quality-improvement experiments ⋮ Process monitoring for mean shifts for multiple stage processes ⋮ Andrews plots for multivariate data: Some new suggestions and applications ⋮ Industrial statistics and its recent contributions to total quality in the Netherlands

This page was built for publication: Modelling Variation in Industrial Experiments