Title :
Comparison of AIC and MDL to the minimum probability of error criterion
Author :
Williams, Douglas B.
Author_Institution :
Georgia Inst. of Technol., Sch. of Electr. Eng., Atlanta, GA, USA
Abstract :
A large variety of model order determination problems involve testing the eigenvalue of a sample covariance matrix to estimate how many of the smallest eigenvalues of the true covariance matrix are equal. Using the theory of multiple hypothesis tests, the author derives the minimum probability of error criterion that is similar to AIC and MDL and is implemented in exactly the same manner, but is designed to minimize the probability of choosing the wrong model order. The basic structure of this test is very similar except for an extra term that increases adaptability and enables this criterion to outperform both AIC and MDL
Keywords :
eigenvalues and eigenfunctions; error statistics; signal processing; AIC; MDL; adaptability; eigenvalue; minimum probability of error criterion; model order determination; multiple hypothesis tests; sample covariance matrix; Chaos; Contracts; Covariance matrix; Density functional theory; Eigenvalues and eigenfunctions; Error analysis; Laboratories; Performance evaluation; Probability; Testing;
Conference_Titel :
Statistical Signal and Array Processing, 1992. Conference Proceedings., IEEE Sixth SP Workshop on
Conference_Location :
Victoria, BC
Print_ISBN :
0-7803-0508-6
DOI :
10.1109/SSAP.1992.246861