Wald statistic for model order selection in superposition models

Ashutosh Sabharwal, Lee Potter

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


A consistent model selection algorithm is presented for superimposed signal models. The proposed method is motivated by the Wald statistic and reduces the computational complexity of procedures based on the minimum description length (MDL) principle. The procedure is suggested when a noncyclostationary signal model or short data length prevents use of covariance rank test. For maximum model-order K, the procedure provides O(K) computational savings over an MDL test. Additionally, a proof establishes the consistency of a least-squares estimator using overparametrized models. Finite sample performance of the proposed model selection method is studied via Monte Carlo simulations for estimating the multipath delays and amplitudes of a chirp signal.

Original languageEnglish (US)
Pages (from-to)956-965
Number of pages10
JournalIEEE Transactions on Signal Processing
Issue number4
StatePublished - Apr 2002


  • Inverse scattering
  • Model order selection
  • Multipath channels
  • Nested nonlinear models
  • Overparametrized models
  • Stochastic complexity
  • Superimposed models
  • Wald statistic

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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