Model selection for nested model classes with cost constraints

A. Sabharwal; L. Potter

Model selection for nested model classes with cost constraints

Research output: Contribution to conference › Paper › peer-review

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)
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
City	Portland, OR, USA
Period	9/14/98 → 9/16/98

ASJC Scopus subject areas

General Engineering

Cite this

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title = "Model selection for nested model classes with cost constraints",

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.",

author = "A. Sabharwal and L. Potter",

year = "1998",

language = "English (US)",

pages = "84--87",

note = "Proceedings of the 1998 9th IEEE SP Workshop on Statistical Signal and Array Processing ; Conference date: 14-09-1998 Through 16-09-1998",

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TY - CONF

T1 - Model selection for nested model classes with cost constraints

AU - Sabharwal, A.

AU - Potter, L.

PY - 1998

Y1 - 1998

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/0032298029

UR - https://www.scopus.com/inward/citedby.url?scp=0032298029&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0032298029

SP - 84

EP - 87

T2 - Proceedings of the 1998 9th IEEE SP Workshop on Statistical Signal and Array Processing

Y2 - 14 September 1998 through 16 September 1998

ER -

Model selection for nested model classes with cost constraints

Abstract

Conference

ASJC Scopus subject areas

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