This paper addresses the problem of model selection. Three different approaches for low order model selection are presented; a modified MDL/AIC based backward elimination approach, a modified F-statistic based backward elimination approach and a bootstrap-based approach. To compare the performances of these approaches, we apply each method to two different linear models; a moving average filter and a recursive filter. First we estimate the model parameters using least squares (LS) techniques in the time domain. Based on these estimates, a bootstrap-based multiple hypothesis test and two modified backward elimination based approaches are then applied to identify the true model, in other words the model corresponding to the true non-zero coefficients. Simulation results demonstrate the power of using each technique for model selection in a low SNR environment. A comparison between the proposed schemes are also presented.
Bootstrap and backward elimination based approaches for model selection
01.01.2003
370027 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Bootstrap and Backward Elimination Based Approaches for Model Selection
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