Search

The article offers the author's insights on the article "Statistical Engineering and Variation Reduction," by Stefan H. Steiner and R. Jock MacKay. He states that Steiner and MacKay's paper are useful in problem solving in terms of reducing variation through statistical engineering (StatEng). The author adds that the article paper also exploits the sequential approach on learning imposed by George Box.

Many experiments in biotechnology exploit the principles and methods of response surface methodology (RSM). The Quality by Design initiative in pharmaceutical development will accelerate this trend. In this article we give a broad picture of the important role played by RSM in biotechnology experimentation. We give brief descriptions of a collection of studies using RSM from recent issues of biotechnology journals, present some of the issues in the design and analysis of experiments for cell media, and look in detail at one of the experiments. [ABSTRACT FROM AUTHOR]

Computer experiments enable scientists to study complex processes by running computer codes that simulate them. We consider here the analysis of data from computer experiments, comparing three methods for non-parametric smoothing of high-dimensional data: Kriging, Multivariate Adaptive Regression Splines (MARS) and Projection Pursuit Regression (PPR). On several data sets of varying complexity, we find that Kriging is consistently the best performer. [ABSTRACT FROM AUTHOR]

The goal of achieving high quality products has led to an emphasis on reducing variation in performance characteristics. It may often happen that one of the product's components is responsible for much of the observed variation. This research is stimulated by the problem of detecting a component that impairs quality by systematically inflating the variance in a product that is assembled from "interchangeable components." We consider the class of "disassembly-reassembly" experiments, in which components are swapped among assemblies. The specific units used in the experiment are sampled from a large population of units, so it is natural to measure the influence of each factor by its variance component. We present the model for these experiments as a special case of the mixed linear model, compare several estimators for the variance components and consider the problem of testing hypotheses to identify troublesome components. [ABSTRACT FROM AUTHOR]

The authors reflect on the additional improvements in Dolt algorithm and its use in Bayesian statistics. They note that the method can be improved through better choice of matrix using the first approximation computation. They suggest that some evaluation sites must be added concerning the placement of nonnegligible mass outside the points by the posterior density based on the nanowire experiment. They add that the approach is considered a useful tool for Bayesian analysis.