Results 181 to 190 of about 101,405 (230)

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.
C. C. Taylor
semanticscholar   +3 more sources

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.
R. Shibata
semanticscholar   +2 more sources

Hellinger distance and Akaike's information criterion for the histogram

Statistics & Probability Letters, 1993
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yuichiro Kanazawa
semanticscholar   +2 more sources

Akaike's Information Criterion

International Encyclopedia of Statistical Science, 2011
H. Akaike
semanticscholar   +2 more sources

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.
K. F. Turkman
semanticscholar   +2 more sources

Akaike’s information criterion

, 2001
D. Arndt
semanticscholar   +3 more sources

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.
openaire   +2 more sources

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
openaire   +1 more source

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
openaire   +1 more source

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.
openaire   +1 more source