Evaluation of Response Surface Designs in Presence of Errors in Factor Levels

Experimenters are often confronted with the problem that errors in setting factor levels cannot be measured. In the robust design scenario, the goal is to determine the design that minimizes the variability transmitted to the response from the variables’ errors. The prediction variance performance of response surface designs with errors is investigated using design efficiency and the maximum and minimum scaled prediction variance. The evaluation and comparison of response surface designs with and without errors in variables are developed for second order designs on spherical regions. The prediction variance and design efficiency results and recommendations for their use are provided.     

Measuring Gross Flows in the Labor Force: An Overview of a Special Conference

The measurement of flows in the American labor market has challenged researchers for years. Several problems, including response variability, rotation group bias, and matching errors, hinder accurate measurement of the flows. The Bureau of the Census and the Bureau of Labor Statistics jointly sponsored a conference to examine these problems, present current research on solutions to the problems, and submit recommendations for improving the data. Recommendations include procedural changes in the Current Population Survey as well as new estimation techniques.     

Reanalyzing Ultimatum Bargaining—Comparing Nondecreasing Curves Without Shape Constraints

We employ a hierarchical Bayesian method with exchangeable prior distributions to estimate and compare similar nondecreasing response curves. A Dirichlet process distribution is assigned to each of the response curves as a first stage prior. A second stage prior is then used to model the hyperparameters. We define parameters which will be used to compare the response curves. A Markov chain Monte Carlo method is applied to compute the resulting Bayesian estimates. To illustrate the methodology, we re-examine data from an experiment designed to test whether experimenter observation influences the ultimatum game. A major restriction of the original analysis was the shape constraint that the present technique allows us to greatly relax. We also consider independent priors and use Bayes factors to compare various models.     

On D-optimal robust second order slope-rotatable designs

Das and Park (2006) introduced slope-rotatable designs overall directions for correlated observations which is known as A-optimal robust slope-rotatable designs. This article focuses D-optimal slope-rotatable designs for second-order response surface model with correlated observations. It has been established that robust second-order rotatable designs are also D-optimal robust slope-rotatable designs. A class of D-optimal robust second-order slope-rotatable designs has been derived for special correlation structures of errors.     

A Bayesian Approach for Multiple Response Surface Optimization in the Presence of Noise Variables

An approach for the multiple response robust parameter design problem based on a methodology by Peterson (2000) is presented. The approach is Bayesian, and consists of maximizing the posterior predictive probability that the process satisfies a set of constraints on the responses. In order to find a solution robust to variation in the noise variables, the predictive density is integrated not only with respect to the response variables but also with respect to the assumed distribution of the noise variables. The maximization problem involves repeated Monte Carlo integrations, and two different methods to solve it are evaluated. A Matlab code was written that rapidly finds an optimal (robust) solution in case it exists. Two examples taken from the literature are used to illustrate the proposed method.     

C194. A nearly flat prior,some unsolved problexs,anb soxe comments about improper priors

Two different approaches to obtaining finite-sample corrections to score tests are the analytical and the computational approaches. The former is based either on a Bartletttype correction to the test statistic or on the inversion of an Edgeworth expansion to its null distribution. The latter, on the other hand, is usually based on a bootstrapping resampling scheme. This paper provides a numerical comparison of the size and power properties of these two approaches both under correct model specification and under model misspecification.     

从读者反应角度重读田纳西·威廉斯的《欲望号街车》

传统批评家们大都把《欲望号街车》看作女主人公布兰奇的命运悲剧。然而,从读者反应角度解读田纳西·威廉斯的《欲望号街车》,不但剧本主角变成男主人公斯坦利,而且整个剧本还呈现出全新的诠释意义,主题从布兰奇的命运悲剧转变成斯坦利的家庭喜剧。读者／观众的阅读空白为新意义的产生提供可能,读者／观众通过情感体验的手段判断自己的道德立场,并最终在感受否定阶段裁决自己的道德倾向。同时,读者反应角度还能映射出田纳西潜意识里对父权制社会的敬畏和对男性主导地位的认同。     

Robust Estimation of Multi-response Surfaces Considering Correlation Structure

Response surfaces express the behavior of responses and can be used for both single and multi-response problems. A common approach to estimate a response surface using experimental results is the ordinary least squares (OLS) method. Since OLS is very sensitive to outliers, some robust approaches have been discussed in the literature. Although there are many methods available in the literature for multiple response optimizations, there are a few studies in model building especially robust models. Assuming correlated responses, in this paper, a robust coefficient estimation method is proposed for multi response problem based on M-estimators. In order to illustrate the performance of the proposed procedure, a contaminated experimental design using a numerical example available in the literature with some modifications is used. Both the classical multivariate least squares method and the proposed robust multivariate approach are used to estimate regression coefficients of multi-response surfaces based on this example. Moreover, a comparison of the proposed robust multi response surface (RMRS) approach with separate robust estimation of single response show that the proposed approach is more efficient.