Results 211 to 220 of about 95,615 (253)

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)
Wei Pan
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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 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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Exponential Smoothing and the Akaike Information Criterion [PDF]

, 2009
Using an innovations state space approach, it has been found that the Akaike information criterion (AIC) works slightly better, on average, than prediction validation on withheld data, for choosing between the various common methods of exponential smoothing for forecasting. There is, however, a puzzle.
Ralph D. Snyder, J. Keith Ord
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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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A modified akaike information criterion

1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes, 1978
A method, closely related to Akaike's Information Criterion (AIC), is introduced that more nearly matches practical methods of estimating the parameters of an autoregressive (AR) model of a stationary time series. The method is computationally similar to AIC, and in preliminary experiments has shown considerable success in identifying AR model orders.
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Properties of the Akaike information criterion

Microelectronics Reliability, 1996
The paper gives the origins of AIC and discusses the main properties of this measure when it is applied to continuous and discrete models. It is illustrated that AIC is not a measure of informativity because it fails to have some expected properties of information measures.
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On the Comparison of Akaike Information Criterion and Consistent Akaike Information Criterion in Selection of an Asymmetric Price Relationship: Bootstrap Simulation Results

2013
Akaike’s Information Criteria provide a basis for choosing between competing approaches to testing for price asymmetry. However, very little research has been undertaken to understand its performance in the price transmission modelling context. In addressing this issue, this paper introduces and applies parametric bootstrap techniques to evaluate the ...
Acquah, H. De-Graft, Acquah, H. De-Graft
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The Akaike Information Criterion with Parameter Uncertainty

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006., 2006
An instance crucial to most problems in signal processing is the selection of the order of a candidate model. Among the different exciting criteria, the two most popular model selection criteria in the signal processing literature have been the Akaike's criterion AIC and the Bayesian information criterion BIC. These criteria are similar in form in that
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