Automatic Transaction of Signal via Statistical Modeling

The statistical information processing can be characterized by the likelihood function de ned by giving an explicit form for an approximation to the true distribution. This mathematical representation, which is usually called a model, is built based on not only the current data but also prior knowledge on the object and the objective of the analysis. Akaike2, 3) showed that the log-likelihood can be consideredas an estimate of the Kullback-Leibler (K-L) information which measures the similarity between the predictive distributionof the modeland the true distribution. Akaike information criterion (AIC) is an estimate of the K-L information and makes it possible to evaluate and compare the goodness of many models objectively. In consequence, the minimum AIC procedure allows us to develop automatic modeling and signal extraction procedures. In this article, we give a simple explanation of statistical modeling based on the AIC and demonstrate four examples of applying the minimum AIC proce...

• Type: Conference
• Year: 1998
• Where: DIS
• Authors: Genshiro Kitagawa, Tomoyuki Higuchi
• Keywords: DIS 1998, Minimum Aic Procedure, Statistical Information Processing, Theoretical Computer Science, True Distribution