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Activity Number: 419
Type: Contributed
Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #316208 View Presentation
Title: Discrepancy-Based Parameter Estimation for Balancing Efficiency and Robustness in Fitting State-Space Models
Author(s): Nan Hu* and Joseph Cavanaugh
Companies: The University of Iowa and The University of Iowa
Keywords: dynamic linear model ; maximum likelihood estimation ; time series analysis
Abstract:

In the state-space modeling framework, parameter estimation is often accomplished by maximizing the innovations Gaussian log-likelihood. The maximum likelihood estimator (MLE) is efficient when the normality assumption is satisfied. However, in the presence of contamination or thicker-tailed noise distributions, the MLE suffers from a lack of robustness. Basu, Harris, Hjort, and Jones (1998) introduced a discrepancy measure (BHHJ) with a nonnegative tuning parameter that controls the trade-off between robustness and efficiency. In this talk, we propose a new parameter estimation procedure based on the BHHJ discrepancy for fitting state-space models. As the tuning parameter is increased, the estimation procedure becomes more robust but less efficient. We investigate the performance of the procedure in a comprehensive simulation study. In addition, we provide guidelines on how to choose an appropriate tuning parameter in practice.


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