Preben Blaesild – författare

The present set of notes grew out of our interest in the study of statistical transformation models, in particular exponential transfor- mation models. The latter class comprises as special cases all fully tractable models for mUltivariate normal observations. The theory of decomposition and invariance of measures provides essential tools for the study of transformation models. While the major aspects of that theory are treated in a number of mathematical monographs, mostly as part of much broader contexts, we have found no single account in the literature which is sufficiently comprehensive for statistical pur- poses. This volume aims to fill the gap and to indicate the usefulness of measure decomposition and invariance theory for the methodology of statistical transformation models. In the course of the work with these notes we have benefitted much from discussions with steen Arne Andersson, J0rgen Hoffmann-J0rgensen and J0rgen Granfeldt Petersen. We are also very indebted to Jette Ham- borg and Oddbj0rg Wethelund for their eminent secretarial assistance.

The use of statistics is fundamental to many endeavors in biology and geology. For students and professionals in these fields, there is no better way to build a statistical background than to present the concepts and techniques in a context relevant to their interests. Statistics with Applications in Biology and Geology provides a practical introduction to using fundamental parametric statistical models frequently applied to data analysis in biology and geology.Based on material developed for an introductory statistics course and classroom tested for nearly 10 years, this treatment establishes a firm basis in models, the likelihood method, and numeracy. The models addressed include one sample, two samples, one- and two-way analysis of variance, and linear regression for normal data and similar models for binomial, multinomial, and Poisson data. Building on the familiarity developed with those models, the generalized linear models are introduced, making it possible for readers to handle fairly complicated models for both continuous and discrete data. Models for directional data are treated as well. The emphasis is on parametric models, but the book also includes a chapter on the most important nonparametric tests.This presentation incorporates the use of the SAS statistical software package, which authors use to illustrate all of the statistical tools described. However, to reinforce understanding of the basic concepts, calculations for the simplest models are also worked through by hand. SAS programs and the data used in the examples and exercises are available on the Internet.