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 (ECEE)

Research output: Contribution to journal › Article

16 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 1 2017

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ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

Cite this

Fortunati, S., Gini, F., Greco, M. S., & Richmond, C. (2017). Performance bounds for parameter estimation under misspecified models: Fundamental findings and applications. IEEE Signal Processing Magazine, 34(6), 142-157. [8103120]. https://doi.org/10.1109/MSP.2017.2738017

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Access to Document

10.1109/MSP.2017.2738017