Performance bounds for parameter estimation under misspecified models: Fundamental findings and applications

Stefano Fortunati; Fulvio Gini; Maria S. Greco; Christ D. Richmond

doi:10.1109/MSP.2017.2738017

Performance bounds for parameter estimation under misspecified models: Fundamental findings and applications

Stefano Fortunati
, Fulvio Gini
, Maria S. Greco
, Christ D. Richmond

University of Pisa
Arizona State University

Résultats de recherche: Contribution à un journal › Article › Revue par des pairs

Résumé

Inferring information from a set of acquired data is the main objective of any signal processing (SP) method. The common problem of estimating the value of a vector of parameters from a set of noisy measurements is at the core of a plethora of scientific and technological advances in recent decades, including wireless communications, radar and sonar, biomedicine, image processing, and seismology.

langue originale	Anglais
Numéro d'article	8103120
Pages (de - à)	142-157
Nombre de pages	16
journal	IEEE Signal Processing Magazine
Volume	34
Numéro de publication	6
Les DOIs	https://doi.org/10.1109/MSP.2017.2738017
état	Publié - 1 nov. 2017
Modification externe	Oui

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10.1109/MSP.2017.2738017

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title = "Performance bounds for parameter estimation under misspecified models: Fundamental findings and applications",

abstract = "Inferring information from a set of acquired data is the main objective of any signal processing (SP) method. The common problem of estimating the value of a vector of parameters from a set of noisy measurements is at the core of a plethora of scientific and technological advances in recent decades, including wireless communications, radar and sonar, biomedicine, image processing, and seismology.",

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Performance bounds for parameter estimation under misspecified models: Fundamental findings and applications

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