A comparison between robust information theoretic estimator and asymptotic maximum likelihood estimator for misspecified model

Xin Zhou; Steven M. Kay

doi:10.1117/12.2304550

27 April 2018 A comparison between robust information theoretic estimator and asymptotic maximum likelihood estimator for misspecified model

Xin Zhou, Steven M. Kay

Author Affiliations +

Proceedings Volume 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII; 106460N (2018) https://doi.org/10.1117/12.2304550
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

A robust information-theoretic estimator (RITE) is based on a non-homogeneous Poisson spectral representation. When an autoregressive (AR) Gaussian wide sense stationary (WSS) process is corrupted by noise, RITE is analyzed and shown by simulation to be more robust to noise than the asymptotic maximum likelihood estimator (MLE). The statistics of RITE and asymptotic MLE are analyzed for the misspecified model. For large data records, RITE and MLE are asymptotically normally distributed. MLE has lower variance, but RITE exhibits much less bias. Simulation examples of a noise corrupted AR process are provided to support the theoretical properties and show the advantage of RITE for low signal-to-noise ratios (SNR).

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Xin Zhou and Steven M. Kay "A comparison between robust information theoretic estimator and asymptotic maximum likelihood estimator for misspecified model", Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 106460N (27 April 2018); https://doi.org/10.1117/12.2304550

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