A Bayesian extension of the minimum AIC procedure of autoregressive model fitting
BiometrikaPublished 1 January 1979
Hirotugu Akaike
Citations604
SJR quartileQ1
SJR score3.60
SNIP2.67
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
Abstract
The proposal of simultaneous use of modified AIC statistics by Bhansali & Downham for the fitting of autoregressive models is reviewed and a Bayesian extension of the minimum AIC procedure is proposed. The practical utility of the procedure is demonstrated by numerical examples.
Keywords
Computer ScienceMathematics
IEEE Transactions on Automatic ControlA new look at the statistical model identification
50,732 Citations1974Hirotugu Akaike
Springer series in statisticsFitting Autoregreesive Models for Prediction
2,043 Citations1998Hirotugu Akaike
This is a preliminary report on a newly developed simple and practical procedure of statistical identification of predictors by using autoregressive models in a stationary time series.
Mathematics in Science and Engineering/Mathematics in science and engineeringCanonical Correlation Analysis of Time Series and the Use of an Information Criterion
664 Citations1976Hirotugu Akaike
The Annals of StatisticsAsymptotically Efficient Selection of the Order of the Model for Estimating Parameters of a Linear Process
556 Citations1980Ritei Shibata
BiometrikaSome properties of the order of an autoregressive model selected by a generalization of Akaike∘s EPF criterion
207 Citations1977R. J. Bhansali, D. Y. Downham
Journal of the American Statistical AssociationA Monte Carlo Comparison of the Regression Method and the Spectral Methods of Prediction
30 Citations1973R. J. Bhansali