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The Sense of Belonging Quotient: Relating Sense of Belonging to Predictors of Scientific Civic Engagement and Science Identity in Civically Engaged Curricula. [PDF]

CBE Life Sci Educ
Alam I, Bilinski T, Bernot KM, Cotter R, Ortiz-Zuazaga E, Vance-Chalcraft HD, Corwin LA.   +6 more
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Model selection using the Akaike information criterion [PDF]

Stata Technical Bulletin, 2000
Zhiqiang Wang
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Marker Selection by Akaike Information Criterion and Bayesian Information Criterion

Genetic Epidemiology, 2001
We carried out a discriminant analysis with identity by descent (IBD) at each marker as inputs, and the sib pair type (affected‐affected versus affected‐unaffected) as the output. Using simple logistic regression for this discriminant analysis, we illustrate the importance of comparing models with different number of parameters.
Li, W., Nyholt, D.R.
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Akaike's Information Criterion in Packer Test Analysis

SPE/EAGE Reservoir Characterization and Simulation Conference, 2009
Abstract Gradient techniques are used predominantly in History Matching and Optimization. In this paper gradient technique was used in estimation of multiple packer test data (permeability distribution of very low permeable formations). A high pressure gas chamber has been released into the formation and pressure changes in this chamber ...
M.M. Rafiee, F. Haefner, H.D. Voigt
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Multi-sample cluster analysis using Akaike's Information Criterion

Annals of the Institute of Statistical Mathematics, 1984
Multi-sample cluster analysis, the problem of grouping samples, is studied from an information-theoretic viewpoint via Akaike's information criterion (AIC). This criterion combines the maximum value of the likelihood with the number of parameters used in achieving that value.
Bozdogan, Hamparsum, Sclove, Stanley L.
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Akaike's Information Criterion and the Histogram

Biometrika, 1987
By interpreting the histogram as a step-function, we explore the use of Akaike's information criterion in an automatic procedure to determine the histogram class width. We obtain an asymptotic relationship and present some results from a small simulation study.
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Comparing time activity curves using the Akaike information criterion

Physics in Medicine and Biology, 2009
The comparison of curves is a common task in many fields of science. Simply comparing the sums of squares or R(2) is not sufficient, and frequently used tests have many disadvantages. The basic idea of the presented method is turning the problem of comparing curves into a problem of model selection using the corrected Akaike Information Criterion. Here,
Peter, Kletting, Thomas, Kull, Sven N, Reske, Gerhard, Glatting   +3 more
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Akaike's Information Criterion in Generalized Estimating Equations

Biometrics, 2001
Summary. Correlated response data are common in biomedical studies. Regression analysis based on the generalized estimating equations (GEE) is an increasingly important method for such data. However, there seem to be few model‐selection criteria available in GEE. The well‐known Akaike Information Criterion (AIC)
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