The segment method as an alternative to minimax in hypothesis testing

Bruce H. Krogh; H. Vincent Poor

doi:10.1016/0020-0255(82)90009-3

The segment method as an alternative to minimax in hypothesis testing

Bruce H. Krogh, H. Vincent Poor

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

An alternative approach to the design of hypothesis tests within modeling uncertainty is proposed. This approach is based on a design technique known as the segment method, which has been established previously as a favorable alternative to the minimax concept in the context of optimal control. The general segment concept is discussed here as it applies to binary decisions made in the presence of unknown prior distribution or parametric uncertainty. In the latter context, several aspects of the design of hypothesis tests on Gaussian data are considered in detail.

Original language	English (US)
Pages (from-to)	9-37
Number of pages	29
Journal	Information Sciences
Volume	27
Issue number	1
DOIs	https://doi.org/10.1016/0020-0255(82)90009-3
State	Published - Jun 1982
Externally published	Yes

All Science Journal Classification (ASJC) codes

Software
Control and Systems Engineering
Theoretical Computer Science
Computer Science Applications
Information Systems and Management
Artificial Intelligence

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10.1016/0020-0255(82)90009-3

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title = "The segment method as an alternative to minimax in hypothesis testing",

abstract = "An alternative approach to the design of hypothesis tests within modeling uncertainty is proposed. This approach is based on a design technique known as the segment method, which has been established previously as a favorable alternative to the minimax concept in the context of optimal control. The general segment concept is discussed here as it applies to binary decisions made in the presence of unknown prior distribution or parametric uncertainty. In the latter context, several aspects of the design of hypothesis tests on Gaussian data are considered in detail.",

author = "Krogh, {Bruce H.} and Poor, {H. Vincent}",

note = "Funding Information: *This research was supported in part by the Joint Services Electronics Program (U.S. Army, U.S. Navy. and U.S. Air Force) under Contract NOOO14-79-C-0424,i n part by the Office of Naval Research under Contract NOOOl4-81-K-0014. and in part by the Air Force Office of Scientific Research under Contract AFOSR 78-3633.",

year = "1982",

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T1 - The segment method as an alternative to minimax in hypothesis testing

AU - Krogh, Bruce H.

AU - Poor, H. Vincent

N1 - Funding Information: *This research was supported in part by the Joint Services Electronics Program (U.S. Army, U.S. Navy. and U.S. Air Force) under Contract NOOO14-79-C-0424,i n part by the Office of Naval Research under Contract NOOOl4-81-K-0014. and in part by the Air Force Office of Scientific Research under Contract AFOSR 78-3633.

PY - 1982/6

Y1 - 1982/6

N2 - An alternative approach to the design of hypothesis tests within modeling uncertainty is proposed. This approach is based on a design technique known as the segment method, which has been established previously as a favorable alternative to the minimax concept in the context of optimal control. The general segment concept is discussed here as it applies to binary decisions made in the presence of unknown prior distribution or parametric uncertainty. In the latter context, several aspects of the design of hypothesis tests on Gaussian data are considered in detail.

AB - An alternative approach to the design of hypothesis tests within modeling uncertainty is proposed. This approach is based on a design technique known as the segment method, which has been established previously as a favorable alternative to the minimax concept in the context of optimal control. The general segment concept is discussed here as it applies to binary decisions made in the presence of unknown prior distribution or parametric uncertainty. In the latter context, several aspects of the design of hypothesis tests on Gaussian data are considered in detail.

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The segment method as an alternative to minimax in hypothesis testing

Abstract

All Science Journal Classification (ASJC) codes

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