This study is undertaken with the objective of investigating the performance of Akaike’s Information Corrected Criterion (AICC) as an order determination criterion for the selection of Autoregressive Moving-average or ARMA (p, q) time series models. A simulation investigation was carried out to determine the probability of the AICC statistic picking up the true model. Results obtained showed that the probability of the AICC criterion picking up the correct model was moderately good. The problem of over parameterization existed but under parameterization was found to be minimal. Hence, for any two comparable models, it is always safe to choose the one with lower order of p and q
A robust version of the Akaike Information Criterion (AIC) [5] is defined to the aim of selecting th...
This is the final version. Available from Hindawi via the DOI in this record. The present paper deal...
We show that analyzing model selection in ARMA time series models as a quadratic discrimination prob...
This study is undertaken with the objective of investigating the performance of Akaike’s Information...
This study is undertaken with the objective of investigating the performance of Akaike’s Information...
This study is undertaken with the objective of investigating the performance of Akaike's Information...
Autoregression-moving average (ARMA) models provide insight into many biological systems. One of the...
Autoregression-moving average (ARMA) models provide insight into many biological systems. One of the...
Autoregression-moving average (ARMA) models provide insight into many biological systems. One of the...
A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregres...
The purpose of this paper is to compare different autoregressive models performance in case of incor...
A robust version of the Akaike Information Criterion (AIC) [5] is defined to the aim of selecting th...
A robust version of the Akaike Information Criterion (AIC) [5] is defined to the aim of selecting th...
A robust version of the Akaike Information Criterion (AIC) [5] is defined to the aim of selecting th...
A robust version of the Akaike Information Criterion (AIC) [5] is defined to the aim of selecting th...
A robust version of the Akaike Information Criterion (AIC) [5] is defined to the aim of selecting th...
This is the final version. Available from Hindawi via the DOI in this record. The present paper deal...
We show that analyzing model selection in ARMA time series models as a quadratic discrimination prob...
This study is undertaken with the objective of investigating the performance of Akaike’s Information...
This study is undertaken with the objective of investigating the performance of Akaike’s Information...
This study is undertaken with the objective of investigating the performance of Akaike's Information...
Autoregression-moving average (ARMA) models provide insight into many biological systems. One of the...
Autoregression-moving average (ARMA) models provide insight into many biological systems. One of the...
Autoregression-moving average (ARMA) models provide insight into many biological systems. One of the...
A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregres...
The purpose of this paper is to compare different autoregressive models performance in case of incor...
A robust version of the Akaike Information Criterion (AIC) [5] is defined to the aim of selecting th...
A robust version of the Akaike Information Criterion (AIC) [5] is defined to the aim of selecting th...
A robust version of the Akaike Information Criterion (AIC) [5] is defined to the aim of selecting th...
A robust version of the Akaike Information Criterion (AIC) [5] is defined to the aim of selecting th...
A robust version of the Akaike Information Criterion (AIC) [5] is defined to the aim of selecting th...
This is the final version. Available from Hindawi via the DOI in this record. The present paper deal...
We show that analyzing model selection in ARMA time series models as a quadratic discrimination prob...