Outperforming completely randomized designs.

Bi-randomization designs have become increasingly popular in industry because some of the factors under investigation are often hard-to-change. It is well-known that the resulting compound symmetric error structure not only affects estimation and inference procedures but also the efficiency of the experimental designs used. In this paper, the use of bi-randomization designs is shown to outperform completely randomized designs in terms of D-efficiency. This result suggests that bi-randomization designs should be considered as an alternative to completely randomized designs even if all experimental factors are easy-to-change.Optimal;

The D-optimal design of blocked and split-plot experiments with mixture components.

So far, the optimal design of blocked and split-plot experiments involving mixture components has received scant attention. In this paper, an easy method to construct efficient blocked mixture experiments in the presence of fixed and/or random blocks is presented. The method can be used when qualitative variables are involved in a mixture experiment as well. It is also shown that orthogonally blocked mixture experiments are highly inefficient compared to D-optimal designs. Finally, the design of a split-plot mixture experiment with process variables is discussed.Design; Fixed and random blocks; Minimum support design; Mixture experiment; Optimal; Optimal design; Orthogonal blocking; Process variables; Processes; Qualitative variables; Split-plot experiment; Variables;

Experimental Design for Sensitivity Analysis, Optimization and Validation of Simulation Models

This chapter gives a survey on the use of statistical designs for what-if analysis in simula- tion, including sensitivity analysis, optimization, and validation/verification. Sensitivity analysis is divided into two phases. The first phase is a pilot stage, which consists of screening or searching for the important factors among (say) hundreds of potentially important factors. A novel screening technique is presented, namely sequential bifurcation. The second phase uses regression analysis to approximate the input/output transformation that is implied by the simulation model; the resulting regression model is also known as a metamodel or a response surface. Regression analysis gives better results when the simu- lation experiment is well designed, using either classical statistical designs (such as frac- tional factorials) or optimal designs (such as pioneered by Fedorov, Kiefer, and Wolfo- witz). To optimize the simulated system, the analysts may apply Response Surface Metho- dology (RSM); RSM combines regression analysis, statistical designs, and steepest-ascent hill-climbing. To validate a simulation model, again regression analysis and statistical designs may be applied. Several numerical examples and case-studies illustrate how statisti- cal techniques can reduce the ad hoc character of simulation; that is, these statistical techniques can make simulation studies give more general results, in less time. Appendix 1 summarizes confidence intervals for expected values, proportions, and quantiles, in termi- nating and steady-state simulations. Appendix 2 gives details on four variance reduction techniques, namely common pseudorandom numbers, antithetic numbers, control variates or regression sampling, and importance sampling. Appendix 3 describes jackknifing, which may give robust confidence intervals.least squares;distribution-free;non-parametric;stopping rule;run-length;Von Neumann;median;seed;likelihood ratio

Optimal designs for conjoint experiments.

Design; Model-sensitive; Optimal; Optimal design; Data;

Using Experiments to Foster Innovation and Improve the Effectiveness of Energy Efficiency Programs

This paper argues that the establishment of a process designed to manage innovation must be developed in California to foster the creation of needed program improvements and develop new and more effective energy efficiency delivery programs. This paper discusses several key institutional problems that must be overcome to achieve significant progress

Optimal Design Generation and Power Evaluation in R: The skpr Package

The R package skpr provides a suite of functions to generate and evaluate experimental designs. Package skpr generates D, I, Alias, A, E, T, and G-optimal designs, and supports custom user-defined optimality criteria, N-level split-plot designs, mixture designs, and design augmentation. Also included are a collection of analytic and Monte Carlo power evaluation functions for normal, non-normal, random effects, and survival models, as well as tools to plot fraction of design space plots and correlation maps. Additionally, skpr includes a flexible framework for the user to perform custom power analyses with external libraries and user-defined functions, as well as a graphical user interface that wraps most of the functionality of the package in a point-and-click web application

The role of statistical methodology in simulation

statistical methods;simulation;operations research

Estimation of Supersonic Stage Separation Aerodynamics of Winged-Body Launch Vehicles Using Response Surface Methods

A collection of statistical and mathematical techniques referred to as response surface methodology was used to estimate the longitudinal stage separation aerodynamic characteristics of a generic, bimese, winged multi-stage launch vehicle configuration using data obtained on small-scale models at supersonic speeds in the NASA Langley Research Center Unitary Plan Wind Tunnel. The simulated Mach 3 staging was dominated by multiple shock wave interactions between the orbiter and booster vehicles throughout the relative spatial locations of interest. This motivated a partitioning of the overall inference space into several contiguous regions within which the separation aerodynamics were presumed to be well-behaved and estimable using cuboidal and spherical central composite designs capable of fitting full second-order response functions. The primary goal was to approximate the underlying overall aerodynamic response surfaces of the booster vehicle in belly-to-belly proximity to the orbiter vehicle using relatively simple, lower-order polynomial functions that were piecewise-continuous across the full independent variable ranges of interest. The quality of fit and prediction capabilities of the empirical models were assessed in detail, and the issue of subspace boundary discontinuities was addressed. The potential benefits of augmenting the central composite designs to full third order using computer-generated D-optimality criteria were also evaluated. The usefulness of central composite designs, the subspace sizing, and the practicality of fitting low-order response functions over a partitioned inference space dominated by highly nonlinear and possibly discontinuous shock-induced aerodynamics are discussed