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

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

Electrical, Computer, and Energy Engineering, School of (IAFSE-ECEE)

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

177 Scopus citations

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.

Original language	English (US)
Article number	8103120
Pages (from-to)	142-157
Number of pages	16
Journal	IEEE Signal Processing Magazine
Volume	34
Issue number	6
DOIs	https://doi.org/10.1109/MSP.2017.2738017
State	Published - Nov 2017

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

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10.1109/MSP.2017.2738017

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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

Abstract

ASJC Scopus subject areas

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