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Journal ArticleDOI

Taguchi's parameter design: a panel discussion

TLDR
A group of practitioners and researchers discuss the role of parameter design and Taguchi's methodology for implementing it and the importance of parameter-design principles with well-established statistical techniques.
Abstract
It is more than a decade since Genichi Taguchi's ideas on quality improvement were inrroduced in the United States. His parameter-design approach for reducing variation in products and processes has generated a great deal of interest among both quality practitioners and statisticians. The statistical techniques used by Taguchi to implement parameter design have been the subject of much debate, however, and there has been considerable research aimed at integrating the parameter-design principles with well-established statistical techniques. On the other hand, Taguchi and his colleagues feel that these research efforts by statisticians are misguided and reflect a lack of understanding of the engineering principles underlying Taguchi's methodology. This panel discussion provides a forum for a technical discussion of these diverse views. A group of practitioners and researchers discuss the role of parameter design and Taguchi's methodology for implementing it. The topics covered include the importance of vari...

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Citations
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Journal Article

The Design and Analysis of Experiments

TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
Journal ArticleDOI

Generalized Linear Models (2nd ed.)

John H. Schuenemeyer
- 01 May 1992 - 
Journal ArticleDOI

Robust Optimization - A Comprehensive Survey

TL;DR: The main focus will be on the different approaches to perform robust optimization in practice including the methods of mathematical programming, deterministic nonlinear optimization, and direct search methods such as stochastic approximation and evolutionary computation.

Response Surface Methodology.

TL;DR: A survey of the various stages in the development of response surface methodology RSM is given in this article, which includes a review of basic experimental designs for fitting linear response surface models, in addition to a description of methods for the determination of optimum operating conditions.
Journal ArticleDOI

Response surface methodology

TL;DR: A survey of the various stages in the development of response surface methodology RSM is provided, organized in three parts that describe the evolution of RSM since its introduction in the early 1950s.
References
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Book

Generalized Linear Models

TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
Journal Article

The Design and Analysis of Experiments

TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
Journal ArticleDOI

A comparison of three methods for selecting values of input variables in the analysis of output from a computer code

TL;DR: In this paper, two sampling plans are examined as alternatives to simple random sampling in Monte Carlo studies and they are shown to be improvements over simple sampling with respect to variance for a class of estimators which includes the sample mean and the empirical distribution function.
Journal ArticleDOI

Multivariate Adaptive Regression Splines

TL;DR: In this article, a new method is presented for flexible regression modeling of high dimensional data, which takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations) are automatically determined by the data.
Journal ArticleDOI

The design and analysis of computer experiments

TL;DR: This paper presents a meta-modelling framework for estimating Output from Computer Experiments-Predicting Output from Training Data and Criteria Based Designs for computer Experiments.
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