Some practical issues in the design and analysis of computer experiments

Deterministic computer simulations of physical experiments are now common techniques in science and engineering. Often, physical experiments are too time consuming, expensive or impossible to conduct. Complex computer models or codes, rather than physical experiments lead to the study of computer experiments, which are used to investigate many scientific phenomena of this nature. A computer experiment consists of a number of runs of the computer code with different input choices. The Design and Analysis of Computer Experiments is a rapidly growing technique in statistical experimental design. This thesis investigates some practical issues in the design and analysis of computer experiments and attempts to answer some of the questions faced by experimenters using computer experiments. In particular, the question of the number of computer experiments and how they should be augmented is studied and attention is given to when the response is a function over time.

[1]  R. Iman,et al.  A distribution-free approach to inducing rank correlation among input variables , 1982 .

[2]  T. J. Mitchell,et al.  Exploratory designs for computational experiments , 1995 .

[3]  M. Kendall Statistical Methods for Research Workers , 1937, Nature.

[4]  Steven M. Bartell,et al.  Simulated transport of polycyclic aromatic hydrocarbons in artificial streams , 1981 .

[5]  D R Griffin,et al.  Letters to the editor. , 1974, Science.

[6]  T J Santner,et al.  Robust optimization of total joint replacements incorporating environmental variables. , 1999, Journal of biomechanical engineering.

[7]  A. Saltelli,et al.  A quantitative model-independent method for global sensitivity analysis of model output , 1999 .

[8]  Kwok-Leung Tsui,et al.  A Minimum Bias Latin Hypercube Design , 2001 .

[9]  Noel A Cressie,et al.  Statistics for Spatial Data. , 1992 .

[10]  William D. Walton ASET-B: A room fire program for personal computers , 1985 .

[11]  Henry P. Wynn,et al.  Screening, predicting, and computer experiments , 1992 .

[12]  Art B. Owen,et al.  Using simulators to model transmitted variability in IC manufacturing , 1989 .

[13]  O. Dykstra The Augmentation of Experimental Data to Maximize [X′X] , 1971 .

[14]  David W. Stroup,et al.  Methods to calculate the response time of heat and smoke detectors installed below large unobstructed ceilings , 1985 .

[15]  T. Sahama,et al.  Sample size considerations and augmentation of computer experiments , 2001 .

[16]  Henry P. Wynn,et al.  Maximum entropy sampling , 1987 .

[17]  李幼升,et al.  Ph , 1989 .

[18]  Robert V. O'Neill,et al.  Error analysis of predicted fate of anthracene in a simulated pond , 1983 .

[19]  Urmila M. Diwekar,et al.  An efficient sampling technique for off-line quality control , 1997 .

[20]  Eva Riccomagno,et al.  Experimental Design and Observation for Large Systems , 1996, Journal of the Royal Statistical Society: Series B (Methodological).

[21]  I. Sobol,et al.  Sensitivity Measures, ANOVA-like Techniques and the Use of Bootstrap , 1997 .

[22]  G. Sposito Ergodicity and the ‘scale effect’ , 1997 .

[23]  William J. Welch,et al.  Computer experiments and global optimization , 1997 .

[24]  A distribution‐free approach to qdantal response assays , 1975 .

[25]  Art B. Owen,et al.  9 Computer experiments , 1996, Design and analysis of experiments.

[26]  T. Simpson A concept exploration method for product family design , 1998 .

[27]  G. Robinson That BLUP is a Good Thing: The Estimation of Random Effects , 1991 .

[28]  Neil A. Butler,et al.  Optimal and orthogonal Latin hypercube designs for computer experiments , 2001 .

[29]  Jack P. C. Kleijnen,et al.  A methodology for the fitting and validation of metamodels in simulation , 2000 .

[30]  Kenny Q. Ye,et al.  Algorithmic construction of optimal symmetric Latin hypercube designs , 2000 .

[31]  C. Currin,et al.  A Bayesian Approach to the Design and Analysis of Computer Experiments , 1988 .