Author
William G. Hunter
Bio: William G. Hunter is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Model building & Design of experiments. The author has an hindex of 31, co-authored 61 publications receiving 15065 citations.
Papers published on a yearly basis
Papers
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TL;DR: In this paper, a 3-yr historical record of sewage treatment plant performance has been evaluated graphically and with time series methodology, and four possible definitions of plant efficiency were defined and studied as well.
31 citations
TL;DR: In this paper, the authors study how and to what extent effluent BOD 5 is related to influent BOD 1 and flow in an activated sludge process. But, the analysis is based on data collected hourly over a 2-week period at a Wisconsin sewage treatment plant and does not need flow as a predictor variable.
29 citations
TL;DR: Evolutionary operation, as originally presented by G. E. P. Box in 1957, is now an accepted means of improving the performance of industrial processes as mentioned in this paper, and numerous articles have been published relating to the successful application of this procedure.
Abstract: Evolutionary operation, as originally presented by G. E. P. Box in 1957, is now an accepted means of improving the performance of industrial processes. Numerous articles have since been published relating to the successful application of this procedure and they indicate that the technique is one of general industrial importance. It is the purpose of this article to review briefly these applications, thus providing a source of references useful both to those familiar with EVOP and those wishing to examine the potential applicability of the method for an existing process.
28 citations
TL;DR: In this article, a method for handling missing data in multiresponse nonlinear model fitting is described and illustrated with examples, where the missing data can be used to improve model fitting.
Abstract: A method for handling missing data in multiresponse nonlinear model fitting is described and illustrated with examples.
25 citations
TL;DR: In this article, the authors present a method to select data points such that the confidence region of the estimated parameters is smaller with each data point than with any other possible data point within the region of experimentation.
Abstract: A method is reviewed which allows data points to be chosen in such a fashion that precise estimates of the parameters in nonlinear reaction rate models can be obtained. This method allows each future data point to be selected such that the confidence region of the estimated parameters is smaller with it than with any other possible data point within the region of experimentation. This procedure is applied for Hougen-Watson models with hypothetical experimental data which were generated with the guidance of an example from the current chemical engineering literature. It is found that, for the same number of data points, the parameters in the model can be estimated eighteen times more precisely by using this suggested experimental design than by another commonly used design. Confidence regions are presented for the parameters of the Hougen-Watson models with two types of designs.
It is found that the positions of the data points in the well-designed experiments are more sensitive to the functional form of the model than to the current estimates of the magnitudes of the parameter values.
23 citations
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Book•
21 Mar 2002TL;DR: An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data is as discussed by the authors, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced.
Abstract: An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature The book is supported by a website that provides all data sets, questions for each chapter and links to software
9,509 citations
Book•
01 Jan 2015TL;DR: This book offers a complete blueprint for structuring projects to achieve rapid completion with high engineering productivity during the research and development phase to ensure that high quality products can be made quickly and at the lowest possible cost.
Abstract: From the Publisher:
Phadke was trained in robust design techniques by Genichi Taguchi, the mastermind behind Japanese quality manufacturing technologies and the father of Japanese quality control. Taguchi's approach is currently under consideration to be adopted as a student protocol with the US govrnment. The foreword is written by Taguchi. This book offers a complete blueprint for structuring projects to achieve rapid completion with high engineering productivity during the research and development phase to ensure that high quality products can be made quickly and at the lowest possible cost. Some topics covered are: orthogonol arrays, how to construct orthogonal arrays, computer-aided robutst design techniques, dynamic systems design methods, and more.
3,928 citations
TL;DR: In this paper, the complex mechanisms of Fenton and Fenton-like reactions and the important factors influencing these reactions, from both a fundamental and practical perspective, in applications to water and soil treatment, are discussed.
Abstract: Fenton chemistry encompasses reactions of hydrogen peroxide in the presence of iron to generate highly reactive species such as the hydroxyl radical and possibly others. In this review, the complex mechanisms of Fenton and Fenton-like reactions and the important factors influencing these reactions, from both a fundamental and practical perspective, in applications to water and soil treatment, are discussed. The review covers modified versions including the photoassisted Fenton reaction, use of chelated iron, electro-Fenton reactions, and Fenton reactions using heterogeneous catalysts. Sections are devoted to nonclassical pathways, by-products, kinetics and process modeling, experimental design methodology, soil and aquifer treatment, use of Fenton in combination with other advanced oxidation processes or biodegradation, economic comparison with other advanced oxidation processes, and case studies.
3,218 citations
TL;DR: In this paper, alternative formulations of Levene's test statistic for equality of variances are found to be robust under nonnormality, using more robust estimators of central location in place of the mean.
Abstract: Alternative formulations of Levene's test statistic for equality of variances are found to be robust under nonnormality. These statistics use more robust estimators of central location in place of the mean. They are compared with the unmodified Levene's statistic, a jackknife procedure, and a χ2 test suggested by Layard which are all found to be less robust under nonnormality.
2,559 citations
Book•
01 Apr 2004
TL;DR: In this paper, the authors present a method for sensitivity analysis of a fish population model using Monte Carlo filtering and variance-based methods, which is based on the Bayesian uncertainty estimation.
Abstract: PREFACE. 1. A WORKED EXAMPLE. 1.1 A simple model. 1.2 Modulus version of the simple model. 1.3 Six--factor version of the simple model. 1.4 The simple model 'by groups'. 1.5 The (less) simple correlated--input model. 1.6 Conclusions. 2. GLOBAL SENSITIVITY ANALYSIS FOR IMPORTANCE ASSESSMENT. 2.1 Examples at a glance. 2.2 What is sensitivity analysis? 2.3 Properties of an ideal sensitivity analysis method. 2.4 Defensible settings for sensitivity analysis. 2.5 Caveats. 3. TEST CASES. 3.1 The jumping man. Applying variance--based methods. 3.2 Handling the risk of a financial portfolio: the problem of hedging. Applying Monte Carlo filtering and variance--based methods. 3.3 A model of fish population dynamics. Applying the method of Morris. 3.4 The Level E model. Radionuclide migration in the geosphere. Applying variance--based methods and Monte Carlo filtering. 3.5 Two spheres. Applying variance based methods in estimation/calibration problems. 3.6 A chemical experiment. Applying variance based methods in estimation/calibration problems. 3.7 An analytical example. Applying the method of Morris. 4. THE SCREENING EXERCISE. 4.1 Introduction. 4.2 The method of Morris. 4.3 Implementing the method. 4.4 Putting the method to work: an analytical example. 4.5 Putting the method to work: sensitivity analysis of a fish population model. 4.6 Conclusions. 5. METHODS BASED ON DECOMPOSING THE VARIANCE OF THE OUTPUT. 5.1 The settings. 5.2 Factors Prioritisation Setting. 5.3 First--order effects and interactions. 5.4 Application of Si to Setting 'Factors Prioritisation'. 5.5 More on variance decompositions. 5.6 Factors Fixing (FF) Setting. 5.7 Variance Cutting (VC) Setting. 5.8 Properties of the variance based methods. 5.9 How to compute the sensitivity indices: the case of orthogonal input. 5.9.1 A digression on the Fourier Amplitude Sensitivity Test (FAST). 5.10 How to compute the sensitivity indices: the case of non--orthogonal input. 5.11 Putting the method to work: the Level E model. 5.11.1 Case of orthogonal input factors. 5.11.2 Case of correlated input factors. 5.12 Putting the method to work: the bungee jumping model. 5.13 Caveats. 6. SENSITIVITY ANALYSIS IN DIAGNOSTIC MODELLING: MONTE CARLO FILTERING AND REGIONALISED SENSITIVITY ANALYSIS, BAYESIAN UNCERTAINTY ESTIMATION AND GLOBAL SENSITIVITY ANALYSIS. 6.1 Model calibration and Factors Mapping Setting. 6.2 Monte Carlo filtering and regionalised sensitivity analysis. 6.2.1 Caveats. 6.3 Putting MC filtering and RSA to work: the problem of hedging a financial portfolio. 6.4 Putting MC filtering and RSA to work: the Level E test case. 6.5 Bayesian uncertainty estimation and global sensitivity analysis. 6.5.1 Bayesian uncertainty estimation. 6.5.2 The GLUE case. 6.5.3 Using global sensitivity analysis in the Bayesian uncertainty estimation. 6.5.4 Implementation of the method. 6.6 Putting Bayesian analysis and global SA to work: two spheres. 6.7 Putting Bayesian analysis and global SA to work: a chemical experiment. 6.7.1 Bayesian uncertainty analysis (GLUE case). 6.7.2 Global sensitivity analysis. 6.7.3 Correlation analysis. 6.7.4 Further analysis by varying temperature in the data set: fewer interactions in the model. 6.8 Caveats. 7. HOW TO USE SIMLAB. 7.1 Introduction. 7.2 How to obtain and install SIMLAB. 7.3 SIMLAB main panel. 7.4 Sample generation. 7.4.1 FAST. 7.4.2 Fixed sampling. 7.4.3 Latin hypercube sampling (LHS). 7.4.4 The method of Morris. 7.4.5 Quasi--Random LpTau. 7.4.6 Random. 7.4.7 Replicated Latin Hypercube (r--LHS). 7.4.8 The method of Sobol'. 7.4.9 How to induce dependencies in the input factors. 7.5 How to execute models. 7.6 Sensitivity analysis. 8. FAMOUS QUOTES: SENSITIVITY ANALYSIS IN THE SCIENTIFIC DISCOURSE. REFERENCES. INDEX.
2,297 citations