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

The R package mice imputes incomplete multivariate data by chained equations. The software mice 1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. mice 1.0 introduced predictor selection, passive imputation and automatic pooling. This article documents mice, which extends the functionality of mice 1.0 in several ways. In mice, the analysis of imputed data is made completely general, whereas the range of models under which pooling works is substantially extended. mice adds new functionality for imputing multilevel data, automatic predictor selection, data handling, post-processing imputed values, specialized pooling routines, model selection tools, and diagnostic graphs. Imputation of categorical data is improved in order to bypass problems caused by perfect prediction. Special attention is paid to transformations, sum scores, indices and interactions using passive imputation, and to the proper setup of the predictor matrix. mice can be downloaded from the Comprehensive R Archive Network. This article provides a hands-on, stepwise approach to solve applied incomplete data problems.

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mice: Multivariate Imputation by Chained Equations in R

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

This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or $N$-way array. Decompositions of higher-order tensors (i.e., $N$-way arrays with $N \geq 3$) have applications in psycho-metrics, chemometrics, signal processing, numerical linear algebra, computer vision, numerical analysis, data mining, neuroscience, graph analysis, and elsewhere. Two particular tensor decompositions can be considered to be higher-order extensions of the matrix singular value decomposition: CANDECOMP/PARAFAC (CP) decomposes a tensor as a sum of rank-one tensors, and the Tucker decomposition is a higher-order form of principal component analysis. There are many other tensor decompositions, including INDSCAL, PARAFAC2, CANDELINC, DEDICOM, and PARATUCK2 as well as nonnegative variants of all of the above. The N-way Toolbox, Tensor Toolbox, and Multilinear Engine are examples of software packages for working with tensors.

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Tensor Decompositions and Applications

Statistics for Spatial Data.

Introduction and Motivation. Order Statistics. Asymptotic Distributions of Maxima and Minima (I.D. Case). Shortcut Procedures: Probability Papers and Least-Square Methods. The Gumbel, Weibull, and Frechet Distributions. Selection of Limit Distributions from Data. Limit Distributions of K-TH Order Statistics. Limit Distributions in the Case of Dependence. Multivariate and Regression Models Related to Extremes. Multivariate Extremes. Appendixes.

Extreme value theory in engineering

Artificial intelligence and expert systems have seen a great deal of research in recent years, much of which has been devoted to methods for incorporating uncertainty into models. This book is devoted to providing a thorough and up-to-date survey of this field for researchers and students.

Expert Systems and Probabilistic Network Models

Preface. I: DATA, INTRODUCTION AND MOTIVATION. 1. Introduction and Motivation. II: PROBABILISTIC MODELS USEFUL IN EXTREMES. 2. Discrete Probabilistic Models. 3. Continuous Probabilistic Models. III: MODEL ESTIMATION, SELECTION, AND VALIDATION. 4. Model Estimation. 5. Model Selection and Validation. IV: EXACT MODELS FOR ORDER STATISTICS AND EXTREMES. 6. Order Statistics. 7. Point Processes and Exact Models. V: ASYMPTOTIC MODELS FOR EXTREMES AND EXCEEDANCES. 8. Limit Distributions of Order Statistics. 9. Limit Distributions of Exceedances. 10. Multivariate Extremes. Appendix: Statistical Tables. Bibliography. Index.

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Extreme value and related models with applications in engineering and science

Desirability functions have been used extensively to simultaneously optimize several responses. Since the original formulation of these functions contains non-differentiable points, only search methods can be used to optimize the overall desirability response. Furthermore, all responses are treated as equally important. We present modified desirability functions that are everywhere differentiable so that more efficient gradient-based optimization methods can be used instead. The proposed functions have the extra flexibility of allowing the analyst to assign different priorities among the responses. The methodology is applied to a wire bonding process that occurs in semiconductor manufacturing, an industrial process where multiple responses are common.

Modified Desirability Functions for Multiple Response Optimization

The generalized Pareto distribution (GPD) was introduced by Pickands to model exceedances over a threshold. It has since been used by many authors to model data in several fields. The GPD has a scale parameter ([sgrave] > 0) and a shape parameter (−∞ 1, the maximum likelihood estimates do not exist, and when k is between 1/2 and 1, they may have problems. Furthermore, for k ≤ −1/2, second and higher moments do not exist, and hence both the method-of-moments (MOM) and the probability-weighted moments (PWM) estimates do not exist. Another and perhaps more serious problem with the MOM and PWM methods is that they can produce nonsensical estimates (i.e., estimates inconsistent with the observed data). In this article we propose a method for estimating the parameters and quantiles of the GPD. The estimators are well defined for all parameter values. They are also easy to compute. Some asymptotic results are provide...

Enrique Castillo

Papers

Extreme value theory in engineering

Expert Systems and Probabilistic Network Models

Extreme value and related models with applications in engineering and science

Modified Desirability Functions for Multiple Response Optimization

Fitting the Generalized Pareto Distribution to Data