TY - GEN
T1 - The akaike information criterion with parameter uncertainty
AU - Seghouane, Abd Krim
PY - 2006
Y1 - 2006
N2 - 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 they consist of data and penalty terms. Different approaches have been used used to derive these criteria. However, none of them take into account the prior information concerning the parameters of the model. In this paper, an new approach for model selection, that takes into account the prior information on the model parameters, is proposed. Using the proposed approach and depending on the nature of the prior on the model parameters, two new information criteria are proposed for univariate linear regression model selection. We use the term "information criteria" because their derivation is based on the Kullback-Leibler divergence.
AB - 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 they consist of data and penalty terms. Different approaches have been used used to derive these criteria. However, none of them take into account the prior information concerning the parameters of the model. In this paper, an new approach for model selection, that takes into account the prior information on the model parameters, is proposed. Using the proposed approach and depending on the nature of the prior on the model parameters, two new information criteria are proposed for univariate linear regression model selection. We use the term "information criteria" because their derivation is based on the Kullback-Leibler divergence.
UR - http://www.scopus.com/inward/record.url?scp=34250650063&partnerID=8YFLogxK
U2 - 10.1109/sam.2006.1706169
DO - 10.1109/sam.2006.1706169
M3 - Conference contribution
SN - 1424403081
SN - 9781424403080
T3 - 2006 IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006
SP - 430
EP - 434
BT - 2006 IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006
PB - IEEE Computer Society
T2 - 4th IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006
Y2 - 12 July 2006 through 14 July 2006
ER -