Last edited by Migis
Tuesday, May 19, 2020 | History

8 edition of Model Selection and Multi-Model Inference found in the catalog.

Model Selection and Multi-Model Inference

by Kenneth P. Burnham

  • 261 Want to read
  • 2 Currently reading

Published by Springer .
Written in English


The Physical Object
Number of Pages496
ID Numbers
Open LibraryOL7448887M
ISBN 100387953647
ISBN 109780387953649

Model selection and multi-model inference. Model selection was conducted using Akaike weights, w i, based on corrected AIC values as recommended by various authors (Wagenmakers & Farrell.   Furthermore, BIC can be derived as a non-Bayesian result. Therefore, arguments about using AIC versus BIC for model selection cannot be from a Bayes versus frequentist perspective. The philosophical context of what is assumed about reality, approximating models, and the intent of model-based inference should determine whether AIC or BIC is by:

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. P. & Anderson, D. R. Model selection and multimodel inference: a practical information-theoretic approach Springer, PS. REVIEW A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion Matthew R. E. Symonds & Adnan Moussalli Received: 19 April /Revised: 19 July /Accepted: 29 July /Published online: 25 August

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). REVIEW AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons Kenneth P. Burnham & David R. Anderson & Kathryn P. Huyvaert.


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Model Selection and Multi-Model Inference by Kenneth P. Burnham Download PDF EPUB FB2

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 by: Model Selection and Multimodel Inference A Practical Information-Theoretic Approach Authors: Burnham, Kenneth P., Anderson, David R.

Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach by Kenneth P. Burnham () [Kenneth P. Burnham;David R. Anderson] on *FREE* shipping on qualifying offers/5(4).

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(12).

Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach by Burnham, Kenneth P., Anderson, David R. () Paperback on *FREE* shipping on qualifying offers.

Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach by Burnham, Kenneth P., Anderson/5(12). Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach. A unique and comprehensive text on the philosophy of model-based data Model Selection and Multi-Model Inference book and strategy for the analysis of empirical data/5.

This is an applied book written primarily for biologists and statisticians wanting to make inferences from multiple models and is suitable as a graduate text or as a reference for professional analysts. 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.

Model selection and multimodel inference: a practical information-theoretic approach 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.

book might be useful as a text for a course for students with substantial. 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 and multi-model inference: a practical information-theoretic approach by Burnham, Kenneth PPages:   Traditional statistical inference can then be based on this selected best model. However, we now emphasize that information-theoretic approaches allow formal inference to be based on more than one model (m- timodel inference).

Such procedures lead to more robust inferences in many cases, and we advocate these approaches throughout the book.5/5(2). 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.

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.

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.

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.

adshelp[at] The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Agreement NNX16AC86ACited by: Model selection and multimodel inference: a practical information-theoretic approach / Kenneth P.

Burnham, David R. Anderson. 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. These methods allow the data-based selection of a “best” model and a ranking and weighting of the remaining models in a pre-de?ned set.

Traditional statistical inference can then be based on this. We recorded, for each month, the aver- we applied multi-model inference through information-theoretic approach to select a set of 'best models' using the MuMIn r package. 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.

Akaike’s information criterion (AIC) is increasingly being used in analyses in the field of ecology. This measure allows one to compare and rank multiple competing models and to estimate which of them best approximates the “true” process underlying the biological phenomenon under study.

Behavioural ecologists have been slow to adopt this statistical tool, perhaps because of unfounded Cited by: 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.