Results 261 to 270 of about 103,075 (296)

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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Conditional Akaike information criterion in the Fay–Herriot model

Statistical Methodology, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Akaike Information Criterion Statistics.

Journal of the Royal Statistical Society. Series A (Statistics in Society), 1988
Daniel G. Brooks, Y. Sakamoto, M. Ishiguro, G. Kitagawa   +3 more
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Selection of the Order of an Autoregressive Model by Akaike's Information Criterion

Biometrika, 1976
SUMMARY The asymptotic distribution is obtained of the order of regression selected by Akaike's information criterion in autoregressive models. The asymptotic quadratic risks of estimates of regression parameters are evaluated when the order is selected by this method. Some results of computational experiments are given.
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Hellinger distance and Akaike's information criterion for the histogram

Statistics & Probability Letters, 1993
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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The choice of extremal models by Akaike's information criterion

Journal of Hydrology, 1985
Abstract We propose Akaike's information criterion for the choice of extremal models and by simulation we analyse its effectiveness in choosing the most likely among the Gumbel, Frechet and Weibull models.
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