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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Statistics for Spatial Data.

On robust estimation of the location parameter

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

In this article, we develop a mixed EWMA-CUSUM control chart for monitoring moderate and/or small shift in the process following the Conway Maxwell Poisson (COM-Poisson) distribution. To determine ...

Mixed EWMA–CUSUM chart for COM-Poisson distribution

Process monitoring using successive sampling and a repetitive scheme

In this paper, a modification is proposed on CSP-T continuous sampling plan, which was developed by Fordice (9). The modified CSP-T continuous sampling plan is considered for which the rules concerning partial inspection depend, in part, on the length of time it takes to decide that the process quality is good enough so that 100% inspection may be suspended (e.g. the time required to find i consecutive items free of defects). The conventional measures of performance for continuous sampling plans such as average outgoing quality, average fraction inspected, probability of acceptance etc. are evaluated for this modified plan using a Markov chain model. Comparisons with other continuous sampling plans are presented. Tables are also presented for the selection the modified CSP-T plan when the AQL or LQL and AOQL are specified.

Modified CSP-T Sampling Procedures for Continuous Production Processes

The purpose of this paper is to design an SkSP-V acceptance sampling plan for assuring percentiles based on a time-truncated life test as the reference plan when the lifetime of a product follows an exponentiated Weibull distribution. Using the non-linear optimization solution, the plan parameters are determined so as to satisfy the producer's and the consumer's risks. Tables are provided for practical use and an example is shown with a real case. Finally, the results are compared with the single sampling plan in terms of the average sample number to demonstrate the efficiency of the proposed plan.

SkSP-V Sampling Plan for the Exponentiated Weibull Distribution

Classifying genes with time-course expression data into one of the several predefined patterns is viewed as a multiple significance testing problem. The proposed approach calculates the p-value of test statistic using the Monte Carlo method and classifies genes by controlling the overall false discovery rate. We also estimate the positive false discovery rate of each pattern. The proposed procedure was applied to a real data set and some numerical experiments using synthetic data are performed.

Chi-Hyuck Jun

Papers

Mixed EWMA–CUSUM chart for COM-Poisson distribution

Process monitoring using successive sampling and a repetitive scheme

Modified CSP-T Sampling Procedures for Continuous Production Processes

SkSP-V Sampling Plan for the Exponentiated Weibull Distribution

Classifying genes according to predefined patterns by controlling false discovery rate