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 needs to be computed. Simulation results are presented to compare computational savings and detection performance for superimposed undamped exponentials in additive noise.
Original language | English (US) |
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Pages | 84-87 |
Number of pages | 4 |
State | Published - 1998 |
Event | Proceedings of the 1998 9th IEEE SP Workshop on Statistical Signal and Array Processing - Portland, OR, USA Duration: Sep 14 1998 → Sep 16 1998 |
Conference
Conference | Proceedings of the 1998 9th IEEE SP Workshop on Statistical Signal and Array Processing |
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City | Portland, OR, USA |
Period | 9/14/98 → 9/16/98 |
ASJC Scopus subject areas
- Engineering(all)