On reparametrization of loss functions used in estimation and the invariance principle

Signal Processing

Volumn 17, Issue 4, 1989, Pages 383-387

  Stoica, Petre ^a   Söderström, Torsten ^b  

^a UNIVERSITY POLITEHNICA OF BUCHAREST   (Romania)

^b UPPSALA UNIVERSITY   (Sweden)

Author keywords

invariance principle; maximum likelihood estimation; parameter estimation; prediction error; reparametrized models; signal processing; System identification

Indexed keywords

MATHEMATICAL TECHNIQUES--ESTIMATION; SIGNAL FILTERING AND PREDICTION;

ESTIMATION THEORY; INVARIANCE PRINCIPLE; LOSS FUNCTIONS; MAXIMUM LIKELIHOOD ESTIMATION; PARAMETER ESTIMATION; REPARAMETRIZED MODELS;

SYSTEMS SCIENCE AND CYBERNETICS;

EID: 0024716230     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/0165-1684(89)90123-0     Document Type: Article

References (10)

1. Exact maximum likelihood parameter estimation of superimposed exponential signals in noise
2. An algorithm for pole-zero modeling and spectral analysis
3. Adaptive algorithms for constrained ARMA signals in the presence of noise
4. Music, maximum likelihood and Cramér-Rao bound
5. Estimation of frequencies of multiple sinusoids making linear prediction perform like maximum likelihood
6. Invariance of maximum likelihood estimation