Cantitate/Preț
Produs

The Design and Analysis of Computer Experiments: Springer Series in Statistics

Autor Thomas J. Santner, Brian J. Williams, William I. Notz
en Limba Engleză Paperback – dec 2010
In the past 15 to 20 years, the computer has become a popular tool for exploring the relationship between a measured response and factors thought to affect the response. In many cases, scientific theories exist that implicitly relate the response to the factors by means of systems of mathematical equations. There also exist numerical methods for accurately solving such equations and appropriate computer hardware and software to implement these methods. In many engineering applications, for example, the relationship is described by a dynamical system and the numerical method is a finite element code. In such situations, these numerical methods allow one to produce computer code that can generate the response corresponding to any given set of values of the factors. This allows one to conduct an "experiment" (called a "computer experiment") to explore the relationship between the response and the factors using the code. Indeed, in some cases computer experimentation is feasible when a properly designed physical experiment (the gold standard for establishing cause and effect) is impossible. For example, the number of input variables may be too large to consider performing a physical experiment or it may simply be economically prohibitive to run an experiment on the scale required to gather sufficient information to answer a particular research question. This book describes methods for designing and analyzing experiments conducted using computer code in lieu of a physical experiment. It discusses how to select the values of the factors at which to run the code (the design of the computer experiment) in light of the research objectives of the experimenter. It also provides techniques for analyzing the resulting data so as to achieve these research goals.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 78131 lei  6-8 săpt.
  Springer – dec 2010 78131 lei  6-8 săpt.
Hardback (1) 90362 lei  6-8 săpt.
  Springer – 9 ian 2019 90362 lei  6-8 săpt.

Din seria Springer Series in Statistics

Preț: 78131 lei

Preț vechi: 95281 lei
-18% Nou

Puncte Express: 1172

Preț estimativ în valută:
14950 15609$ 12373£

Carte tipărită la comandă

Livrare economică 04-18 aprilie

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9781441929921
ISBN-10: 1441929924
Pagini: 300
Ilustrații: XII, 284 p.
Dimensiuni: 155 x 235 x 16 mm
Greutate: 0.42 kg
Ediția:Softcover reprint of hardcover 1st ed. 2003
Editura: Springer
Colecția Springer
Seria Springer Series in Statistics

Locul publicării:New York, NY, United States

Public țintă

Research

Cuprins

1 Physical Experiments and Computer Experiments.- 2 Preliminaries.- 3 Predicting Output from Computer Experiments.- 4 Additional Topics in Prediction Methodology.- 5 Space-Filling Designs for Computer Experiments.- 6 Some Criterion-based Experimental Designs.- 7 Sensitivity Analysis, Validation, and Other Issues.- A List of Notation.- A.1 Abbreviations.- A.2 Symbols.- B Mathematical Facts.- B.1 The Multivariate Normal Distribution.- B.3 Some Results from Matrix Algebra.- C PErK: Parametric Empirical Kriging.- C.1 Introduction.- C.2 PErK Job File Options and Output.- C.3 Examples.- References.- Author Index.

Recenzii

From the reviews:
"This is quite a unique book and may fill a void in the design of experiments literature." Techonmetrics, November 2004
"This book will be a valuable reference for for any statistitican who is collaborating with scientists who use computer experiments or is interested in pursuing research in the area." Biometrics, March 2005
"This book describes methods for designing and analyzing experiments conducted using computer program to replace a physical experiment. … To the best of my knowledge, there has been no book yet written in the area of computer experiment. … Therefore, this is quite a unique book and may fill a void in the design of experiments literature. As mentioned in the Preface, this book has tried to keep the mathematics at the level of readers with master’s-level training in statistics." (Lih-Yuan Deng, Technometrics, Vol. 46 (4), November, 2004)
"The book by Thomas Santner et al. illustrates the usefulness ofcomputer models and statistical methodologies to extract information in stimulated data … . Computer modeling has been challenging to the practitioners, and this book eases these challenges with the exposure of basic ideas and daunting formulas. This well written book seven chapters … . The references are exhaustive and current." (Ramalingam Shanmugam, Journal of Statistical Computation and Simulation, Vol. 75 (2), February, 2005)

Textul de pe ultima copertă

The computer has become an increasingly popular tool for exploring the relationship between a measured response and factors thought to affect the response.  In many cases, the basis of a computer model is a mathematical theory that implicitly relates the response to the factors. A computer model becomes possible given suitable numerical methods for accurately solving the mathematical system and appropriate computer hardware and software to implement the numerical methods. For example, in many engineering applications, the relationship is described by a dynamical system and the numerical method is a finite element code. The resulting computer "simulator" can generate the response corresponding to any given set of values of the factors. This allows one to use the code to conduct a "computer experiment" to explore the relationship between the response and the factors. In some cases, computer experimentation is feasible when a properly designed physical experiment (the gold standard forestablishing cause and effect) is impossible; the number of input variables may be too large to consider performing a physical experiment, or power studies may show it is economically prohibitive to run an experiment on the scale required to answer a given research question.
This book describes methods for designing and analyzing experiments that are conducted using a computer code rather than a physical experiment. It discusses how to select the values of the factors at which to run the code (the design of the computer experiment) in light of the research objectives of the experimenter.  It also provides techniques for analyzing the resulting data so as to achieve these research goals. It illustrates these methods with code that is available to the reader at the companion web site for the book.
Thomas Santner has been a professor in the Department of Statistics at The Ohio State University since 1990. At Ohio State, he has served as department Chair and Director of thedepartment's Statistical Consulting Service.  Previously, he was a professor in the School of Operations Research and Industrial Engineering at Cornell University. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and is an elected ordinary member of the International Statistical Institute. He visited Ludwig Maximilians Universität in Munich, Germany on a Fulbright Scholarship in 1996-97.
Brian Williams has been an Associate Statistician at the RAND Corporation since 2000. His research interests include experimental design, computer experiments, Bayesian inference, spatial statistics and statistical computing. He holds a Ph.D. in statistics from The Ohio State University.
William Notz is a professor in the Department of Statistics at The Ohio State University.  At Ohio State, he has served as acting department chair, associate dean of the College of Mathematical and Physical Sciences, and as director of the department's Statistical
Consulting Service.  He has also served as Editor of the journal Technometrics and is a Fellow of the American Statistical Association.

 

Notă biografică

 
​Thomas J.  Santner is Professor Emeritus in the Department of Statistics at The Ohio State University.  At Ohio State, he has served as department Chair and Director of the Department's Statistical Consulting Service. Previously, he was a professor in the School of Operations Research and Industrial Engineering at Cornell University. His research interests include the design and analysis of experiments, particularly those involving computer simulators, Bayesian inference, and the analysis of discrete response data.   He is a Fellow of the American Statistical Association, the Institute of Mathematical Statistics, the American Association for the Advancement of Science, and is an elected ordinary member of the International Statistical Institute.  He has held visiting appointments at the National Cancer Institute, the University of Washington, Ludwig Maximilians Universität (Munich, Germany), the National Institute of Statistical Science (NISS), and the Isaac Newton Institute (Cambridge, England).  
 
Brian J. Williams has been Statistician at the Los Alamos National Laboratory RAND Corporation since 2003. His research interests include experimental design, computer experiments, Bayesian inference, spatial statistics and statistical computing. Williams was named a Fellow of the American Statistical Association in 2015 and is also the recipient of the Los Alamos Achievement Award for his leadership role in the Consortium for Advanced Simulation of Light Water Reactors (CASL) Program. He holds a doctorate in statistics from The Ohio State University.
 
William I.  Notz is Professor Emeritus in the Department of Statistics at The Ohio State University.  At Ohio State, he has served as acting department chair, associate dean of the College of Mathematical and Physical Sciences, and as director of the department's StatisticalConsulting Service. His research focuses on experimental designs for computer experiments and he is particularly interested in sequential strategies for selecting points at which to run a computer simulator in order to optimize some performance measure related to the objectives of the computer experiment.  A Fellow of the American Statistical Association, Notz has also served as Editor of the journals Technometrics and the Journal of Statistics Education.


Caracteristici

New to this revised and expanded edition: An expanded presentation of basic material on computer experiments and Gaussian processes with additional simulations and examples A new comparison of plug-in prediction methodologies for real-valued simulator output An enlarged discussion of space-filling designs including Latin Hypercube designs (LHDs), near-orthogonal designs, and nonrectangular regions A chapter length description of process-based designs for optimization, to improve good overall fit, quantile estimation, and Pareto optimization A new chapter describing graphical and numerical sensitivity analysis tools Substantial new material on calibration-based prediction and inference for calibration parameters Lists of software that can be used to fit models discussed in the book to aid practitioners