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

How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize 'best-practice' data analysis procedures for scientists facing this challenge.

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Generalized linear mixed models: a practical guide for ecology and evolution

Statistics for Spatial Data.

Regression models are frequently used to develop diagnostic, prognostic, and health resource utilization models in clinical, health services, outcomes, pharmacoeconomic, and epidemiologic research, and in a multitude of non-health-related areas. Regression models are also used to adjust for patient heterogeneity in randomized clinical trials, to obtain tests that are more powerful and valid than unadjusted treatment comparisons.

/pdf/regression-modeling-strategies-1jyn0f4r32.pdf

Regression Modeling Strategies

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

Principles of data mining

Generalized Linear Modelling.- Discrete Data.- Fitting and Comparing Probability Distributions.- Growth Curves.- Time Series.- Survival Data.- Event Histories.- Spatial Data.- Normal Models.- Dynamic Models.

http://www.leg.ufpr.br/lib/exe/fetch.php/wiki:internas:biblioteca:lindsey-glm.pdf

Applying generalized linear models

PART 1: MODEL BUILDING PART 2: INFERENCE PART 3: APPROXIMATIONS PART 4: DECISIONS PART 5: EXAMPLES PART 6: APPENDICES

Parametric Statistical Inference

Basic concepts Fundamentals of modelling Heterogeneous populations Longitudinal studies Overdispersion Longitudinal discrete data Frailty Event histories. Appendices: data tables programs used.

Models for Repeated Measurements

This second edition of Models for Repeated Measurements has been comprehensively revised and updated, taking into account the huge amount of research that has been carried out in the subject in recent years. A wide variety of useful new models is now available, models that have revolutionized the analysis of such data. The second edition contains three new chapters on models for continuous non-normal data, on various design issues specific to repeated measurements, and on missing data and dropouts. Exercises have been added at the ends of most chapters, and the programming functions needed to carry out the analyses in the book are publicly available. Models for Repeated Measurements is an essential reference for research statisticians in agriculture, medicine, economics, and psychology, and for the many consulting statisticians who want an up-to-date expository account of this important topic. The book is organized into four parts. In the first part, the general context of repeated measurements is presented. The three basic types of response variables, continuous (normal), categorical and count, and duration, are introduced. There is a discussion of the ways in which such repeated observations are interdependent. The book also develops a framework for constructing suitable models, with the introduction of the necessary concepts of multivariate distributions and stochastic processes. In the following three parts, a large number of specific examples, including data tables, is presented to illustrate the models available. Each of these parts corresponds to one of the types of responses mentioned above. FROM REVIEWS OF THE FIRST EDITION . . . a timely book . . . useful both as a graduate text and as a consulting source. Statistical Methods in Medical Research

When testing for a treatment effect or a difference among groups, the distributional assumptions made about the response variable can have a critical impact on the conclusions drawn. For example, controversy has arisen over transformations of the response (Keene). An alternative approach is to use some member of the family of generalized linear models. However, this raises the issue of selecting the appropriate member, a problem of testing non-nested hypotheses. Standard model selection criteria, such as the Akaike information criterion (AIC), can be used to resolve problems. These procedures for comparing generalized linear models are applied to checking for difference in T4 cell counts between two disease groups. We conclude that appropriate model selection criteria should be specified in the protocol for any study, including clinical trials, in order that optimal inferences can be drawn about treatment differences.

https://faculty.washington.edu/heagerty/Courses/b571/homework/Lindsey-Jones-1998.pdf

James K. Lindsey

Papers

Applying generalized linear models

Parametric Statistical Inference

Models for Repeated Measurements

Models for Repeated Measurements

Choosing among generalized linear models applied to medical data