Cover of: Model selection and multimodel inference | Kenneth P. Burnham

Model selection and multimodel inference

a practical information-theoretic approach
  • 488 Pages
  • 3.28 MB
  • 553 Downloads
  • English
by
Springer , New York
Biology -- Mathematical models, Mathematical stati
StatementKenneth P. Burnham, David R. Anderson.
ContributionsAnderson, David R., Burnham, Kenneth P.
Classifications
LC ClassificationsQH323.5 .B87 2002
The Physical Object
Paginationxxvi, 488 p. :
ID Numbers
Open LibraryOL15550780M
ISBN 100387953647
LC Control Number2001057677
OCLC/WorldCa48557578

Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach 2nd Edition by Kenneth P. Burnham (Author)Cited by: S- ond, concepts related to making formal inferences from more than one model (multimodel inference) have been emphasized throughout the book, but p- ticularly in Chapters 4, 5, and 6.

Third, new technical material has been added to Chapters 5 and 6. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach by Burnham, Kenneth P., Anderson, David R.

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Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach by Burnham, Kenneth P., Anderson/5(12). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach Hardcover – December 4, Hardcover – January 1, out of 5 stars 12 ratings See all 15 formats and editions Hide other formats and editions/5(12).

Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach by Kenneth P. Burnham () Hardcover – January 1, out of 5 stars 12 ratings See all 15 formats and editions Hide other formats and editions/5(12). Chapters 2 and 4 have been streamlined in view of the detailed theory provided in Chapter 7.

S- ond, concepts related to making formal inferences from more than one model (multimodel inference) have been emphasized throughout the book, but p- ticularly in Chapters 4, 5, and 6. Third, new technical material has been added to Chapters 5 and 6. 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. DOI: / Corpus ID: Model selection and multimodel inference: a practical information-theoretic approach @article{BurnhamModelSA, title={Model selection and multimodel inference: a practical information-theoretic approach}, author={Kenneth P.

Burnham and David R. Anderson}, journal={Journal of Wildlife Management}, year={}, volume={67}, pages={} }. Author Burnham, Kenneth P. Title Model selection and multimodel inference: a practical information-theoretic approach / Kenneth P. Burnham, David R. Anderson. ond, concepts related to making formal inferences from more than one model (multimodel inference) have been emphasized throughout the book, but par-ticularly in Chapters 4, 5, and 6.

Third, new technical material has been added to Chapters 5 and 6. Well over new references to the technical literature are given. Burnham, Kenneth P. Model selection and inference Bookplateleaf Boxid IA Camera Sony Alpha-A (Control) Collection_set trent External-identifier urn:oclc:record Foldoutcount 0 Grant_report Arcadia # Identifier modelselectionmuburn Identifier-ark ark://t9m40n Invoice Isbn Lccn Pages:   Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach.

A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data/5. The second edition of this book is unique in that it focuses on methods for making formal statistical inference from all the models in an a priori set (Multi-Model Inference).

A philosophy is presented for model-based data analysis and a general strategy outlined for the analysis of empirical data.

Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Model Selection and Multimodel Inference.: We wrote this book to introduce graduate students and research workers in various scienti?c disciplines to the use of information-theoretic approaches in the analysis of empirical data.5/5(2).

Model Selection and Multi-model Inference 1. Lab 14 – Model Selection and Multimodel Inference November 26 & 27, FANR Richard Chandler and Bob Cooper 2. Today’s Topics 1 Model Fitting 2 Model Selection 3 Multi-model Inference 3. Today’s Topics 1 Model Fitting 2 Model Selection 3 Multi-model Inference 4.

Chapters 2 and 4 have been streamlined in view of the detailed theory provided in Chapter 7. S- ond, concepts related to making formal inferences from more than one model (multimodel inference) have been emphasized throughout the book, but p- ticularly in Chapters 4, 5, and 6.

Third, new technical material has been added to Chapters 5 and 6. S- ond, concepts related to making formal inferences from more than one model (multimodel inference) have been emphasized throughout the book, but p- ticularly in Chapters 4, 5, and 6.

Third, new technical material has been added to Chapters 5 and 6. At D-RUG this week Rosemary Hartman presented a really useful case study in model selection, based on her work on frog habitat. Here is her code run through ‘knitr’.

Original code and data are posted here. (yes, I am just doing this for the flying monkey) Editor’s note: we’re giving away flying monkey dolls from our sponsor, Revolution Analytics, to all our D-RUG presenters.

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/5(11).

A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data.

Description Model selection and multimodel inference PDF

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/5(9).

Chapters 2 and 4 have been streamlined in view of the detailed theory provided in Chapter 7. S- ond, concepts related to making formal inferences from more than one model (multimodel inference) have been emphasized throughout the book, but p- ticularly in Chapters 4, 5, and 6.

Third, new technical material has been added to Chapters 5 and /5(2). Model Selection and Multimodel Inference by Kenneth P. Burnham,available at Book Depository with free delivery worldwide/5(23). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach and a great selection of related books, art and collectibles available now at - Model Selection and Multimodel Inference: a Practical Information-theoretic Approach by Burnham, Kenneth P ; Anderson, David R - AbeBooks.

This is an excellent book on model selection and multi-model inference. It covers in great detail the underlying theoretical and philosophical foundations for model selection and provides practical examples with sufficient degree of detail so you could replicate the results on your own/5.

In particular, are there professors of statistics (or other good students of statistics) who explicitly recommended the book as a useful summary of knowledge on using AIC for model selection. Reference: (1) Burnham, K. & Anderson, D. Model selection and multimodel inference: a practical information-theoretic approach Springer, PS.

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/5(11).

Model selection uncertainty and multimodel inference in partial least squares structural equation modeling (PLS-SEM) Recent research has emphasized the efficacy of Information Theoretic model selection criteria in partial least squares structural equation modeling (PLS-SEM), which has gained massive dissemination in a variety of fields.

REVIEW AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons Kenneth P.

Burnham & David R. Anderson & Kathryn P. Huyvaert. "Bolstered by a new chapter and an additional pages, this very specialized book is now quite a sizable affair in its second edition. Subtitled ‘A Practical Information-Theoretic Approach,’ the book is built on the use of the Kullback-Leibler distance approach for multimodel inference.

Model Selection and Multi-Model Inference by David. Anderson ISBN ISBN Hardcover; New York, America.: Springer, December 4, ; ISBN. adshelp[at] The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Agreement NNX16AC86ACited by: A potential concern about our approach is the use of the AICc for comparing models.

Details Model selection and multimodel inference PDF

Although such an approach is an appropriate, accepted technique (40) and is widely used for model selection e.g.5. Model Selection and Multimodel Inference. Model selection and evaluation are important in biomedical research because many variations of models are Cited by: