Praise for the First Edition"The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy ...to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities."--TechnometricsGeneralized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Main.
Response surface methodology Myers, Raymond H; Montgomery, Douglas C; Anderson-Cook, Christine M
2016/01/01, 2016, 2016-01-04
eBook
Praise for the Third Edition: "This new third edition has been substantially rewritten and updated with new topics and material, new examples and exercises, and to more fully illustrate modern ...applications of RSM." - Zentralblatt Math Featuring a substantial revision, the Fourth Edition of Response Surface Methodology: Process and Product Optimization Using Designed Experiments presents updated coverage on the underlying theory and applications of response surface methodology (RSM). Providing the assumptions and conditions necessary to successfully apply RSM in modern applications, the new edition covers classical and modern response surface designs in order to present a clear connection between the designs and analyses in RSM. With multiple revised sections with new topics and expanded coverage, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, Fourth Edition includes: Many updates on topics such as optimal designs, optimization techniques, robust parameter design, methods for design evaluation, computer-generated designs, multiple response optimization, and non-normal responses Additional coverage on topics such as experiments with computer models, definitive screening designs, and data measured with error Expanded integration of examples and experiments, which present up-to-date software applications, such as JMP®, SAS, and Design-Expert®, throughout An extensive references section to help readers stay up-to-date with leading research in the field of RSM An ideal textbook for upper-undergraduate and graduate-level courses in statistics, engineering, and chemical/physical sciences, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, Fourth Edition is also a useful reference for applied statisticians and engineers in disciplines such as quality, process, and chemistry.
Response surface methodology (RSM) is a collection of statistical design and numerical optimization techniques used to optimize processes and product designs. The original work in this area dates ...from the 1950s and has been widely used, especially in the chemical and process industries. The last 15 years have seen the widespread application of RSM and many new developments. In this review paper we focus on RSM activities since 1989. We discuss current areas of research and mention some areas for future research.
This paper is a reflection on where response surface methodology (RSM) is at this point and what will likely be future directions. The emphasis in the last two decades on robust parameter design has ...brought attention to RSM as an alternative methodology for variance reduction and process improvement. While computer generated design technology has been beneficial to those who are interested in constructing RSM designs, changes are needed in this area to allow consideration of design robustness rather than design optimality. RSM is moving into areas involving the use of generalized linear models (GLM's), and optimal RS designs for these areas are either difficult or impossible to implement by the user. Example applications of GLM's include logistic and Poisson regression. Other RSM areas that will enjoy use by practitioners in the twenty-first century include multiple responses and nonparametric and semiparametric methods. In addition, design and analysis techniques for cases where natural restrictions in randomization occur need to be addressed further and communicated to users.
Robust Parameter Design: A Review Robinson, Timothy J.; Borror, Connie M.; Myers, Raymond H.
Quality and reliability engineering international,
February 2004, Letnik:
20, Številka:
1
Journal Article
Dementia is a chronic condition in the elderly and depression is often a concurrent symptom. As populations continue to age, accessible and useful tools to screen for cognitive function and its ...associated symptoms in elderly populations are needed. The aim of this study was to test the reliability and validity of a new internet-based assessment battery for screening mood and cognitive function in an elderly population. Specifically, the Helping Hand Technology (HHT) assessments for depression (HHT-D) and global cognitive function (HHT-G) were evaluated in a sample of 57 elderly participants (22 male, 35 female) aged 59-85 years. The study sample was categorized into three groups: 1) dementia (n = 8; Mini-Mental State Exam (MMSE) score 10-24), 2) mild cognitive impairment (n = 24; MMSE score 25-28), and 3) control (n = 25; MMSE score 29-30). Test-retest reliability (Pearson correlation coefficient, r) and internal consistency reliability (Cronbach's alpha, α) of the HHT-D and HHT-G were assessed. Validity of the HHT-D and HHT-G was tested via comparison (Pearson r) to commonly used pencil-and-paper based assessments: HHT-D versus the Geriatric Depression Scale (GDS) and HHT-G versus the MMSE. Good test-retest (r = 0.80; p < 0.0001) and acceptable internal consistency reliability (α= 0.73) of the HHT-D were established. Moderate support for the validity of the HHT-D was obtained (r = 0.60 between the HHT-D and GDS; p < 0.0001). Results indicated good test-retest (r = 0.87; p < 0.0001) and acceptable internal consistency reliability (α= 0.70) of the HHT-G. Validity of the HHT-G was supported (r = 0.71 between the HHT-G and MMSE; p < 0.0001). In summary, the HHT-D and HHT-G were found to be reliable and valid computerized assessments to screen for depression and cognitive status, respectively, in an elderly sample.
Generalized Linear Models Myers, Raymond H; Montgomery, Douglas C; Vining, G. Geoffrey ...
2010, 2012-01-20, Letnik:
791
eBook
Praise for the First Edition "The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy ...to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities." -Technometrics Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Maintaining the same nontechnical approach as its predecessor, this update has been thoroughly extended to include the latest developments, relevant computational approaches, and modern examples from the fields of engineering and physical sciences. This new edition maintains its accessible approach to the topic by reviewing the various types of problems that support the use of GLMs and providing an overview of the basic, related concepts such as multiple linear regression, nonlinear regression, least squares, and the maximum likelihood estimation procedure. Incorporating the latest developments, new features of this Second Edition include: A new chapter on random effects and designs for GLMs A thoroughly revised chapter on logistic and Poisson regression, now with additional results on goodness of fit testing, nominal and ordinal responses, and overdispersion A new emphasis on GLM design, with added sections on designs for regression models and optimal designs for nonlinear regression models Expanded discussion of weighted least squares, including examples that illustrate how to estimate the weights Illustrations of R code to perform GLM analysis The authors demonstrate the diverse applications of GLMs through numerous examples, from classical applications in the fields of biology and biopharmaceuticals to more modern examples related to engineering and quality assurance. The Second Edition has been designed to demonstrate the growing computational nature of GLMs, as SAS®, Minitab®, JMP®, and R software packages are used throughout the book to demonstrate fitting and analysis of generalized linear models, perform inference, and conduct diagnostic checking. Numerous figures and screen shots illustrating computer output are provided, and a related FTP site houses supplementary material, including computer commands and additional data sets. Generalized Linear Models, Second Edition is an excellent book for courses on regression analysis and regression modeling at the upper-undergraduate and graduate level. It also serves as a valuable reference for engineers, scientists, and statisticians who must understand and apply GLMs in their work.
Variance Dispersion Graphs (VDGs) are useful summaries for comparing competing designs on a fixed design space. However, they do not give all useful information about the prediction capability of a ...design. We propose the Fraction of Design Space (FDS) technique, which addresses some of the shortcomings of VDGs. The new technique gives the researcher more detailed information by quantifying the fraction of design space where the scaled prediction variance (SPV) is less than or equal to any pre-specified value. The FDS graph gives the researcher information about the distribution of the SPV in the region based on the ranges and proportions of possible SPV values. Several variations on the graph, including plotting the variance of the estimated mean response, are also presented to allow for specialized consideration of different designs. The FDS technique complements the use of VDGs to give the researcher more insight into the prediction capability of a design. Several standard designs with different numbers of factors are studied with both methods.
Robust Parameter Design (RPD) has been used extensively in industrial experiments since its introduction by Genichi Taguchi. RPD has been studied and applied, in most cases, assuming a linear model ...under standard assumptions. More recently, RPD has been considered in a generalized linear model (GLM) setting. In this paper, we apply a general dual-response approach when using RPD in the case of a GLM. We motivate the need for exploring both the process mean and process variance by discussing situations when a compromise between the two is necessary. Several examples are provided in order to further motivate the need for applying a dual-response approach when applying RPD in the case of a GLM.
A Tutorial on Generalized Linear Models Myers, Raymond H.; Montgomery, Douglas C.
Journal of quality technology,
07/1997, Letnik:
29, Številka:
3
Journal Article
Recenzirano
Situations in which the observations are not normally distributed arise frequently in the quality engineering field. The standard approach to the analysis of such responses is to transform the ...response into a new quantity that behaves more like a normal random variable. An alternative approach is to use an analysis procedure based on the generalized linear model (GLM), where a nonnormal error distribution and a function that links the predictor to the response may be specified. We present an introduction to the GLM, and show how such models may be fit. We present the GLM as an analog to the normal theory linear model. The usefulness of this approach is illustrated with examples.
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