Model selection for nested model classes with cost constraints

Author

Sabharwal, Ashutosh ; Potter, Lee

Author_Institution

Dept. of Electr. Eng., Ohio State Univ., Columbus, OH, USA

fYear

1998

fDate

14-16 Sep 1998

Firstpage

84

Lastpage

87

Abstract

For model selection with nested classes, we propose to minimize Rissanen´s stochastic complexity with a constraint on expected computational cost. The proposed solution uses the Wald statistic and dynamic programming for order selection, such that maximum likelihood estimates of only a small subset of hypothesized models need to be computed. Simulation results are presented to compare computational savings and detection performance for superimposed undamped exponentials in additive noise

Keywords

Bayes methods; computational complexity; dynamic programming; maximum likelihood estimation; minimisation; noise; signal detection; stochastic processes; Bayes risk; Rissanen stochastic complexity; Wald statistic; additive noise; complexity minimization; computational savings; cost constraints; detection performance; dynamic programming; expected computational cost; hypothesized models; maximum likelihood estimates; model selection; nested model classes; order selection; superimposed undamped exponentials; Costs; Density measurement; Integrated circuit modeling; Maximum likelihood estimation; Parameter estimation; Statistical analysis; Statistical distributions; Statistics; Stochastic processes; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal and Array Processing, 1998. Proceedings., Ninth IEEE SP Workshop on

Conference_Location

Portland, OR

Print_ISBN

0-7803-5010-3

Type

conf

DOI

10.1109/SSAP.1998.739340

Filename

739340