Eric R. Ziegel

Handbook of Simulation

Objects. The ~ b s t rac t ~ b j ect forms the fundamental base class for the entire design and all other classes are derived from this base class. The Abstract Object class defines and characterizes all the essential properties every class in this 404 OBJECT-ORIENTED SIMULATION

Geostatistical Software Library and User's Guide

This book will be an important text to most of geostatisticians, including graduate students and experts in the field of practical geostatistics. The guts of this volume are the two highdensity IBM disks which come with it and contain 37 programs which can be run in both UNIX and DOS environments but are not machine specific. The programs are aimed at three major areas of geostatistics: quantifying spatial variability (variograms), generalized linear regression techniques (kriging), and stochastic simulation. In all there are some 80 source files included with the distribution diskettes. The programs are not execuable but require to be compiled before running them. A machine with a fortran compiler is required. The intent of the authors is to make this suite of programs accessible to anyone who wants to use them. The source code of these programs has been assembled, developed, tested, and tried at Stanford University over a period of some 12 years. Though this library of programs is not intended as a commercial product it represents a gold mine to those who need a jump start into the field of geostatistics.

A Step-by-Step Approach to Using the SAS System for Univariate and Multivariate Statistics

(1995). A Step-by-Step Approach to Using the SAS System for Univariate and Multivariate Statistics. Technometrics: Vol. 37, No. 4, pp. 471-471.

Survey measurement and process quality

Partial table of contents: QUESTIONNAIRE DESIGN. From Theoretical Concept to Survey Question (J. Hox). Designing Rating Scales for Effective Measurement in Surveys (J. Krosnick & L. Fabrigar). DATA COLLECTION. Developing a Speech Recognition Application for Survey Research (B. Blyth). Children as Respondents: Methods for Improving Data Quality (J. Scott). POST SURVEY PROCESSING AND OPERATIONS. Integrated Control Systems for Survey Processing (J. Bethlehem). QUALITY ASSESSMENT AND CONTROL. Continuous Quality Improvement in Statistical Agencies (D. Morganstein & D. Marker). ERROR EFFECTS ON ESTIMATION, ANALYSES, AND INTERPRETATION. Categorical Data Analysis and Misclassification (J. Kuha & C. Skinner). Index.

Adequacy of sample size in health studies

Part 1 Statistical methods for sample size determination: the one sample problem the two sample problem sample size for case-control studies sample size determination for cohort studies lot quality assurance sampling the incidence density sample size for continuous response variables sample size for sample surveys. Part 2 Foundations of sampling and statistical theory: the population the sample sampling distribution characteristics of estimates of population parameters hypothesis testing two sample confidence intervals and hypothesis tests epidemiologic study design basis sampling concepts.

Mastering Data Mining

This book had an interesting origin: Previously the authors wrote Berry and Linoff (1997), reported for Technometrics by Ziegel (2001). As they note in the introduction (p. xvii) to this new book, a lot has changed with the technology in that period of time. In addition, they note, “We want to address the needs of practitioners on both the business and the technical sides” (ibid.). The book arrived as I was departing for an international conference and a site visit concerning data mining projects, so I read most of it on the transAtlantic crossing. It is an easy read. Technical details are not eliminated, but everything is done to make this book totally accessible to anyone in the organization who might need background or have any kind of participation in a data mining project. The book has three parts. The first part gives all the background on the subject. The first of the four chapters here gives both the business and the technical basis for data mining. The second chapter presents the premise that organizations should treat data mining as a core competency. It should be noted that this perspective is set forth by two authors who are part of a consulting business. The third chapter gives an overview of the data-mining process. The fourth chapter is about customers. The subtitle for the book is The Art and Science of Customer Relationship Management, a perspective that influenced the selection of illustrations and case studies but otherwise did not detract from the value of the book for applications that do not involve customers. The second part has four chapters that provide a readable overview of data-mining methodology. The first of these chapters discusses three major techniques—k-means clustering, decision trees, and neural networks. Each topic is handled by discussing the most common approach for using the technique. The next chapter considers the collecting, organizing, and managing of data. There follows a chapter on building predictive models. The two interesting topics in this chapter are the division of the data into test, training, and validation sets and the developing of multiple models for one application. The last chapter in this part has four case studies concerned with the setting up of data mining environments. The last part of the book has seven case studies. All but one of these are devoted to customer-relationship applications. There is a chapter on improving manufacturing processes that discusses data mining projects at R. R. Donnelley and Time-Warner. Both applications involve the improving of problems in printing plants. These are moderately useful in helping the novice understand how to be a useful participant in a data mining project. This book deals in passing with the relationship of both statistics and statisticians to the data mining process. It certainly promotes a role for the statistically knowledgeable participant in the data mining effort. This is actually a good background book for statisticians, despite the lack of any references of any kind. Statisticians will learn the process and find direct and indirect insights into their possible roles in data mining efforts.

Handbook of sequential analysis

Sequential analysis refers to the body of statistical theory and methods where the sample size may depend in a random manner on the accumulating data. A formal theory in which optimal tests are derived for simple statistical hypotheses in such a framework was developed by Abraham Wald in the early 1

geoENV VII: Geostatistics for Environmental Applications

We propose a hierarchical model coupled to geostatistics to deal with a non-gaussian data distribution and take explicitly into account complex spatial structures (i.e. trends, patchiness and random fluctuations). A common characteristic of animal count data is a distribution that is both zero-inflated and heavy tailed. In such cases, empirical variograms are no more robust and most structural analyses result in poor and noisy estimated spatial variogram structures. Thus kriged maps feature a broad variance of prediction. Moreover, due to the heterogeneity of wildlife population habitats, a nonstationary model is often required. To avoid these difficulties, we propose a hierarchical model that assumes that the count data follow a Poisson distribution given a theoretical sighting density which is a latent variable to be estimate. This density is modelled as the product of a positive long range trend by a positive stationary random field, characterized by a unit mean and a variogram function. A first estimate of the drift is used to obtain an estimate of the variogram of residuals including a correction term for variance coming from the Poisson distribution and weights due to the non-constant spatial mean. Then a kriging procedure similar to a modified universal kriging is implemented to directly map the latent density from raw count data. An application on fin whale data illustrates the effectiveness of the method in mapping animal density in a context that is presumably non-stationary. E. Bellier and P. Monestiez Biostatistique et Processus Spatiaux, INRA, Domaine Saint-Paul, Site Agroparc, 84914 Avignon cedex 9, France E. Bellier ( ) Norwegian Institute for Nature Research NINA, NO-7485 Trondheim, NORWAY e-mail: edwige.bellier@nina.no C. Guinet Centre d’Etudes Biologiques de Chize, CNRS, 79360 Villiers-en-Bois, France P.M. Atkinson and C.D. Lloyd (eds.), geoENV VII – Geostatistics for Environmental Applications, Quantitative Geology and Geostatistics 16, DOI 10.1007/978-90-481-2322-3 1, c Springer Science+Business Media B.V. 2010 1

The balanced scorecard

Los autores Robert S. Kaplan y David P. Norton, proponen a los directivos empresariales de cualquier tipo de organización, la utilización de esta teoría de vanguardia, ya comprobada a nivel mundial, que denominan “The Balanced Scorecard” para lograr que la organización en cuestión pueda motivar a su personal y alcanzar los objetivos de la misión empresarial, no siendo solamente un sistema de medición que canaliza aspectos sinergéticos, habilidades gerenciales y conocimiento puntual dirigido a alcanzar las metas fijadas a largo plazo.

Practical Nonparametric and Semiparametric Bayesian Statistics

I Dirichlet and Related Processes.- 1 Computing Nonparametric Hierarchical Models.- 1.1 Introduction.- 1.2 Notation and Perspectives.- 1.3 Posterior Sampling in Dirichlet Process Mixtures.- 1.4 An Example with Poisson-Gamma Structure.- 1.5 An Example with Normal Structure.- 2 Computational Methods for Mixture of Dirichlet Process Models.- 2.1 Introduction.- 2.2 The Basic Algorithm.- 2.3 More Efficient Algorithms.- 2.4 Non-Conjugate Models.- 2.5 Discussion.- 3 Nonparametric Bayes Methods Using Predictive Updating.- 3.1 Introduction.- 3.2 Onn=1.- 3.3 A Recursive Algorithm.- 3.4 Interval Censoring.- 3.5 Censoring Example.- 3.6 Mixing Example.- 3.7 Onn= 2.- 3.8 Concluding Remarks.- 4 Dynamic Display of Changing Posterior in Bayesian Survival Analysis.- 4.1 Introduction and Summary.- 4.2 A Gibbs Sampler for Censored Data.- 4.3 Proof of Proposition 1.- 4.4 Importance Sampling.- 4.5 The Environment for Dynamic Graphics.- 4.6 Appendix: Completion of the Proof of Proposition 1.- 5 Semiparametric Bayesian Methods for Random Effects Models.- 5.1 Introduction.- 5.2 Normal Linear Random Effects Models.- 5.3 DP priors in the Normal Linear Random Effects Model.- 5.4 Generalized Linear Mixed Models.- 5.5 DP priors in the Generalized Linear Mixed Model.- 5.6 Applications.- 5.7 Discussion.- 6 Nonparametric Bayesian Group Sequential Design.- 6.1 Introduction.- 6.2 The DP Mixing Approach Applied to the Group Sequential Framework.- 6.3 Model Fitting Techniques.- 6.4 Implementation of the Design.- 6.5 Examples.- II Modeling Random Functions.- 7 Wavelet-Based Nonparametric Bayes Methods.- 7.1 Introduction.- 7.2 Discrete Wavelet Transformations.- 7.3 Bayes and Wavelets.- 7.4 Other Problems.- 8 Nonparametric Estimation of Irregular Functions with Independent or Autocorrelated Errors.- 8.1 Introduction.- 8.2 Nonparametric Regression for Independent Errors.- 8.3 Nonparametric Regression for Data with Autocorrelated Errors.- 9 Feedforward Neural Networks for Nonparametric Regression.- 9.1 Introduction.- 9.2 Feed Forward Neural Networks as Nonparametric Regression Models.- 9.3 Variable Architecture FFNNs.- 9.4 Posterior Inference with the FFNN Model.- 9.5 Examples.- 9.6 Discussion.- III Levy and Related Processes.- 10 Survival Analysis Using Semiparametric Bayesian Methods.- D. Sinha.- D. Dey.- 10.1 Introduction.- 10.2 Models.- 10.3 Prior Processes.- 10.4 Bayesian Analysis.- 10.5 Further Readings.- 11 Bayesian Nonparametric and Covariate Analysis of Failure Time Data.- 11.1 Introduction.- 11.2 Cox Model with Beta Process Prior.- 11.3 The Computational Model.- 11.4 Illustrative Analysis.- 11.5 Conclusion.- 12 Simulation of Levy Random Fields.- 12.1 Introduction and Overview.- 12.2 Increasing Independent-Increment Processes: A New Look at an Old Idea.- 12.3 Example: Gamma Variates, Processes, and Fields.- 12.4 Inhomogeneous Levy Random Fields.- 12.5 Comparisons with Other Methods.- 12.6 Conclusions.- 13 Sampling Methods for Bayesian Nonparametric Inference Involving Stochastic Processes.- 13.1 Introduction.- 13.2 Neutral to the Right Processes.- 13.3 Mixtures of Dirichlet Processes.- 13.4 Conclusions.- 14 Curve and Surface Estimation Using Dynamic Step Functions.- 14.1 Introduction.- 14.2 Some Statistical Problems.- 14.3 Some Spatial Statistics.- 14.4 Prototype Prior.- 14.5 Posterior Inference.- 14.6 Example in Intensity Estimation.- 14.7 Discussion.- IV Prior Elicitation and Asymptotic Properties 15 Prior Elicitation for Semiparametric Bayesian Survival Analysis.- 15.1 Introduction.- 15.2 The Method.- 15.3 Sampling from the Joint Posterior Distribution of(ss? ao).- 15.4 Applications to Variable Selection.- 15.5 Myeloma Data.- 15.6 Discussion.- 16 Asymptotic Properties of Nonparametric Bayesian Procedures.- 16.1 Introduction.- 16.2 Frequentist or Bayesian Asymptotics?.- 16.3 Consistency.- 16.4 Consistency in Bellinger Distance.- 16.5 Other Asymptotic Properties.- 16.6 The Robins-Ritov Paradox.- 16.7 Conclusion.- V Case Studies.- 17 Modeling Travel Demand in Portland, Oregon.- 17.1 Introduction.- 17.2 The Data.- 17.3 Poisson/Gamma Random Field Models.- 17.4 The Computational Scheme.- 17.5 Posterior Analysis.- 17.6 Discussion.- 18 Semiparametric PK/PD Models.- 18.1 Introduction.- 18.2 A Semiparametric Population Model.- 18.3 Meta-analysis Over Related Studies.- 18.4 Discussion.- 19 A Bayesian Model for Fatigue Crack Growth.- 19.1 Introduction.- 19.2 The Model.- 19.3 A Markov Chain Monte Carlo Method.- 19.4 An Example: Growth of Crack Lengths.- 19.5 Discussion.- 20 A Semiparametric Model for Labor Earnings Dynamics.- 20.1 Introduction.- 20.2 Longitudinal Earnings Data.- 20.3 A Parametric Random Effects Model.- 20.4 A Semiparametric Model.- 20.5 Predictive Distributions.- 20.6 Conclusion.