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This book showcases the innovative research of Professor Skovgaard, by providing in one place a selection of his most important and influential papers. Introductions by colleagues set in context the highlights, key achievements, and impact, of each work.This book provides a survey of the field of asymptotic theory and inference as it was being pushed forward during an exceptionally fruitful time. It provides students and researchers with an overview of many aspects of the field.

This textbook for a second course in basic statistics for undergraduates or first-year graduate students introduces linear regression models and describes other linear models including Poisson regression, logistic regression, proportional hazards regression, and nonparametric regression. Numerous examples drawn from the news and current events with an emphasis on health issues illustrate these concepts. Assuming only a pre-calculus background, the author keeps equations to a minimum and demonstr...

This volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation. Introducing dynamic modeling for GLMs and containing over 1000 references and equations, Generalized Linear Models considers parametric and semiparametric approaches to overdispersed GLMs, presents methods of analyzing correlated binary data using latent variables. It also p...

Large sets of petroleum fluid data exist for the various reservoir conditions and properties that occur in practice. Petroleum Reservoir Fluid Property Correlations, written by three internationally well-known and respected petroleum engineers, is the result of years of exhaustive research that gathered data sets from databases all over the world. The data were then compared against the results of many published correlations of fluid properties in order to find the ""best in class"" required in...

Dallas E. Johnson uses real-life examples to explain the when, why, how, and what of numerous multivariate methods, stressing the importance and practical applications of each method. The result? You come away with a practical understanding of and an ability to do multivariate analysis. By providing annotated SPSS and SAS output along with the computer commands used to produce them, Johnson guides you in relating the results of statistical computing packages to multivariate concepts, and teaches...

From a preeminent authority-a modern and applied treatment of multiway data analysis This groundbreaking book is the first of its kind to present methods for analyzing multiway data by applying multiway component techniques. Multiway analysis is a specialized branch of the larger field of multivariate statistics that extends the standard methods for two-way data, such as component analysis, factor analysis, cluster analysis, correspondence analysis, and multidimensional scaling to multiway data...

After Karl Joreskog's first presentation in 1970, Structural Equation Modelling or SEM has become a main statistical tool in many fields of science. It is the standard approach of factor analytic and causal modelling in such diverse fields as sociology, education, psychology, economics, management and medical sciences. In addition to an extension of its application area, Structural Equation Modelling also features a continual renewal and extension of its theoretical background. The sixteen contr...

Sample surveys form the most widely used method of collecting quantitative social data and offer extensive opportunities for analyzing social phenomena. However, the direct application of standard methods of statistical analysis, devised historically within different traditions of data collection such as experimentation, may be inappropriate and misleading. This volume discusses appropriate principles and methods for the analysis of surveys, such as stratified multi-stage sampling, as well as th...

Provides a systematic account of the subject area, concentrating on the most recent advances in the field. While the focus is on practical considerations, both theoretical and practical issues are explored. Among the advances covered are: regularized discriminant analysis and bootstrap-based assessment of the performance of a sample-based discriminant rule and extensions of discriminant analysis motivated by problems in statistical image analysis. Includes over 1,200 references in the bibliograp...

Multivariate methods are now widely used in the quantitative sciences as well as in statistics because of the ready availability of computer packages for performing the calculations. While access to suitable computer software is essential to using multivariate methods, using the software still requires a working knowledge of these methods and how t

Starting with the basic linear model where the design and covariance matrices are of full rank, this book demonstrates how the same statistical ideas can be used to explore the more general linear model with rank-deficient design and/or covariance matrices. The unified treatment presented here provides a clearer understanding of the general linear model from a statistical perspective, thus avoiding the complex matrix-algebraic arguments that are often used in the rank-deficient case. Elegant geo...

This book is an introduction to the principles and methodology of modern multivariate statistical analysis. It is written for the user and potential user of multivariate techniques as well as for postgraduate students coming to the subject for the first time. The emphasis is problem-orientated and stresses geometrical intuition in preference to algebraic manipulation. Mathematical sections, which are not essential for a practical understanding of the techniques, are clearly indicated so that the...