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

Maximizing data information requires careful selection, termed design, of the points at which data are observed. Experimental design is reviewed here for broad classes of data collection and analysis problems, including: fractioning techniques based on orthogonal arrays, Latin hypercube designs and their variants for computer experimentation, efficient design for data mining and machine learning applications, and sequential design for active learning. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

Experimental design

Control of Metastasis by NK Cells

Screening designs help assess the relative impact of a large number of factors. Experimenters often prefer quantitative factors to have three levels rather than two, but common screening designs use only two factors. This article proposes a new class of..

A Class of Three-Level Designs for Definitive Screening in the Presence of Second-Order Effects

THE best adjective to describe this work is \"sweep11 ing.\" The range of subject matter is so broad that it can almost be described as containing everything except fuzzy set theory. Included are explicit discussions of the basics of probability (relegated to an appendix); the practice of problem solving; testing hypotheses about statistical parameters; calculating and interpreting confidence limits; tolerance limits and prediction limits; setting up and interpreting control charts; design of experiments; analysis of variance; line and surface fitting; and maximum likelihood procedures. If you can think of something that is not in this list, then it probably means I have overlooked it.

Experiments: Planning, Analysis, and Parameter Design Optimization

Inflammation is recognized as one of the drivers of cancer. Yet, the individual immune components that possess pro- and anti-tumorigenic functions in individual cancers remain largely unknown. NKG2D is a potent activating immunoreceptor that has emerged as an important player in inflammatory disorders besides its well-established function as tumour suppressor. Here, we provide genetic evidence of an unexpected tumour-promoting effect of NKG2D in a model of inflammation-driven liver cancer. Compared to NKG2D-deficient mice, NKG2D-sufficient mice display accelerated tumour growth associated with, an increased recruitment of memory CD8+T cells to the liver and exacerbated pro-inflammatory milieu. In addition, we show that NKG2D contributes to liver damage and consequent hepatocyte proliferation known to favour tumorigenesis. Thus, the NKG2D/NKG2D-ligand pathway provides an additional mechanism linking chronic inflammation to tumour development in hepatocellular carcinoma. Our findings expose the need to selectively target the types of cancer that could benefit from NKG2D-based immunotherapy.

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The immunoreceptor NKG2D promotes tumour growth in a model of hepatocellular carcinoma.

Small, balanced, efficient and near rotatable central composite designs

In this paper we present an effective algorithm for the construction and the identification of two-level nonisomorphic orthogonal arrays. Using this algorithm, we identify and list a full catalogue of nonisomorphic orthogonal arrays with parameters OA(24,7,2,t), OA(28,6,2,t) and OA(32,6,2,t), t ≥ 2. Some statistical properties of these designs are also considered.

An effective step-down algorithm for the construction and the identification of nonisomorphic orthogonal arrays

Robust parameter design, originally proposed by Taguchi [System of Experimental Design, Vols. I and II, UNIPUB, New York, 1987], is an offline production technique for reducing variation and improving a product's quality by using product arrays. However, the use of the product arrays results in an exorbitant number of runs. To overcome this drawback, several scientists proposed the use of combined arrays, where the control and noise factors are combined in a single array. In this paper, we use non-isomorphic orthogonal arrays as combined arrays, in order to identify a model that contains all the main effects (control and noise), their control-by-noise interactions and their control-by-control interactions with high efficiency. Some cases where the control-by-control-noise are of interest are also considered.

P. Angelopoulos

Papers

The immunoreceptor NKG2D promotes tumour growth in a model of hepatocellular carcinoma.

Small, balanced, efficient and near rotatable central composite designs

An effective step-down algorithm for the construction and the identification of nonisomorphic orthogonal arrays

Some robust parameter designs from orthogonal arrays

Run orders for efficient two level experimental plans with minimum factor level changes robust to time trends