Maximum likelihood estimators and worst case optimal algorithms for system identification

R. Tempo; G. W. Wasilkowski

doi:10.1016/0167-6911(88)90016-3

Maximum likelihood estimators and worst case optimal algorithms for system identification

R. Tempo, G. W. Wasilkowski

Computer Science

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

26 Scopus citations

Abstract

For system identification problems with stochastic noise, maximum likelihood estimators are frequently used. If noise is deterministic, worst case optimal algorithms should be considered. In this paper we study the following problem: Under what circumstances are maximum likelihood estimators optimal in the worst case?

Original language	English
Pages (from-to)	265-270
Number of pages	6
Journal	Systems and Control Letters
Volume	10
Issue number	4
DOIs	https://doi.org/10.1016/0167-6911(88)90016-3
State	Published - Apr 1988

Bibliographical note

Funding Information:
* This research was developed while Dr. Tempo was visiting the Department of Computer Science, Columbia University supported by a NATO-CNR advanced fellowship and by funds of Ministero della Pubblica Istruzione. ** Supported in part by the National Science Foundation under Grant DCR-86-03674.

Keywords

Maximum likelihood estimators
Noisy information
Optimal algorithms
System identification
Worst case

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science
Mechanical Engineering
Electrical and Electronic Engineering

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10.1016/0167-6911(88)90016-3

Cite this

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title = "Maximum likelihood estimators and worst case optimal algorithms for system identification",

abstract = "For system identification problems with stochastic noise, maximum likelihood estimators are frequently used. If noise is deterministic, worst case optimal algorithms should be considered. In this paper we study the following problem: Under what circumstances are maximum likelihood estimators optimal in the worst case?",

keywords = "Maximum likelihood estimators, Noisy information, Optimal algorithms, System identification, Worst case",

author = "R. Tempo and Wasilkowski, {G. W.}",

note = "Funding Information: * This research was developed while Dr. Tempo was visiting the Department of Computer Science, Columbia University supported by a NATO-CNR advanced fellowship and by funds of Ministero della Pubblica Istruzione. ** Supported in part by the National Science Foundation under Grant DCR-86-03674.",

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AU - Tempo, R.

AU - Wasilkowski, G. W.

N1 - Funding Information: * This research was developed while Dr. Tempo was visiting the Department of Computer Science, Columbia University supported by a NATO-CNR advanced fellowship and by funds of Ministero della Pubblica Istruzione. ** Supported in part by the National Science Foundation under Grant DCR-86-03674.

PY - 1988/4

Y1 - 1988/4

N2 - For system identification problems with stochastic noise, maximum likelihood estimators are frequently used. If noise is deterministic, worst case optimal algorithms should be considered. In this paper we study the following problem: Under what circumstances are maximum likelihood estimators optimal in the worst case?

AB - For system identification problems with stochastic noise, maximum likelihood estimators are frequently used. If noise is deterministic, worst case optimal algorithms should be considered. In this paper we study the following problem: Under what circumstances are maximum likelihood estimators optimal in the worst case?

KW - Maximum likelihood estimators

KW - Noisy information

KW - Optimal algorithms

KW - System identification

KW - Worst case

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U2 - 10.1016/0167-6911(88)90016-3

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M3 - Article

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JO - Systems and Control Letters

JF - Systems and Control Letters

IS - 4

ER -

Maximum likelihood estimators and worst case optimal algorithms for system identification

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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