Model Selection and Multimodel Inference (Hardcover)

Kenneth P. Burnham

ISBN 10: 0387953647 / ISBN 13: 9780387953649

Verlag: Springer-Verlag New York Inc., New York, NY, 2002

Beschreibung

Hardcover. This book is unique in that it covers the philosophy of model-based data analysis and a strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. Kullback-Leibler Information represents a fundamental quantity in science and is Hirotugu Akaike's basis for model selection. The maximized log-likelihood function can be bias-corrected to provide an estimate of expected, relative Kullback-Leibler information. This leads to Akaike's Information Criterion (AIC) and various extensions. These are relatively simple and easy to use in practice. The information theoretic approaches provide a unified and rigorous theory, an extension of likelihood theory, an important application of information theory, and are objective and practical to employ across a very wide class of empirical problems.Model selection, under the information theoretic approach presented here, attempts to identify the (likely) best model, orders the models from best to worst, and measures the plausibility ("calibration") that each model is really the best as an inference. Model selection methods are extended to allow inference from more than a single "best" model. The book presents several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. This is an applied book written primarily for biologists and statisticians using models for making inferences from empirical data. People interested in the empirical sciences will find this material useful as it offers an alternative to hypothesis testing and Bayesian. Statisticians and applied scientists often must select a model to fit empirical data. This book introduces researchers and graduate students in many areas to an information criterion approach, first introduced by Hirotugu Akaike in 1973. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Bestandsnummer des Verkäufers 9780387953649

Bibliografische Details

• Titel: Model Selection and Multimodel Inference (Hardcover)
• Verlag: Springer-Verlag New York Inc., New York, NY
• Erscheinungsjahr: 2002
• Sprache: Englisch
• ISBN-10: 0387953647
• ISBN-13: 9780387953649
• Einband: Hardcover
• Auflage: 2. Auflage
• Zustand: new

Über diesen Titel

Inhaltsangabe

A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.

Über die Autorin bzw. den Autor

Charlotte y Peter Fiell son dos autoridades en historia, teoría y crítica del diseño y han escrito más de sesenta libros sobre la materia, muchos de los cuales se han convertido en éxitos de ventas. También han impartido conferencias y cursos como profesores invitados, han comisariado exposiciones y asesorado a fabricantes, museos, salas de subastas y grandes coleccionistas privados de todo el mundo. Los Fiell han escrito numerosos libros para TASCHEN, entre los que se incluyen 1000 Chairs, Diseño del siglo XX, El diseño industrial de la A a la Z, Scandinavian Design y Diseño del siglo XXI.