THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. 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. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

The purpose of this article is to provide a survey of the various stages in the development of response surface methodology RSM. The coverage of these stages is organized in three parts that describe the evolution of RSM since its introduction in the early 1950s. Part I covers the period, 1951-1975, during which the so-called classical RSM was developed. This 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. Part II, which covers the period, 1976-1999, discusses more recent modeling techniques in RSM, in addition to a coverage of Taguchi's robust parameter design and its response surface alternative approach. Part III provides a coverage of further extensions and research directions in modern RSM. This includes discussions concerning response surface models with random effects, generalized linear models, and graphical techniques for comparing response surface designs. Copyright © 2010 John Wiley & Sons, Inc.

Response Surface Methodology.

Statistical design and analysis of experiments , Statistical design and analysis of experiments , کتابخانه دیجیتال جندی شاپور اهواز

http://centlib.ajums.ac.ir//multiMediaFile/2224724-4-1.pdf

Statistical design and analysis of experiments

In this paper, we provide a review of statistical methods that are useful in conducting computer experiments. Our focus is on the task of metamodeling, which is driven by the goal of optimizing a complex system via a deterministic simulation model. However, we also mention the case of a stochastic simulation, and examples of both cases are discussed. The organization of our review first presents several engineering applications, it then describes approaches for the two primary tasks of metamodeling: (i) selecting an experimental design; and (ii) fitting a statistical model. Seven statistical modeling methods are included. Both classical and newer experimental designs are discussed. Finally, our own computational study tests the various metamodeling options on two two-dimensional response surfaces and one ten-dimensional surface.

A review on design, modeling and applications of computer experiments

Response surface design evaluation and comparison

Scope of the Monograph * Optimal Regression Designs in Symmetric Domains * Optimal Regression Designs in Asymmetric Factor Domains * Optimal Regression Designs for Covariates' Models with Structured Intercept Parameter * Stochastic Distance Optimality * Designs in the Presence of Trends * Additional Selected Topics

Topics in Optimal Design

Box and Draper (1975) proposed a criterion of robustness against the presence of wild observations for the problem of estimation of Xβ in the response surface model E(Y) = Xβ, D(Y) = σ2.I. Gopalau and Dey (1976) elttended this concept to block designs setting, for the problem of estimation of σ2. In the present note an attempt has been made to examine the robustness of block designs for the problem of estimation of any full set of orthonormal varietal contrasts.

On Robust Designs

Optimal experimental designs for models with covariates

Optimum designs for optimum mixtures

Optimal designs in growth curve models: Part I Correlated model for linear growth: Optimal designs for slope parameter estimation and growth prediction

Nripes Kumar Mandal

Papers

Topics in Optimal Design

On Robust Designs

Optimal experimental designs for models with covariates

Optimum designs for optimum mixtures

Optimal designs in growth curve models: Part I Correlated model for linear growth: Optimal designs for slope parameter estimation and growth prediction