Parameter Bounds on Estimation Accuracy Under Model Misspecification

  title={Parameter Bounds on Estimation Accuracy Under Model Misspecification},
  author={Christ D. Richmond and Larry L. Horowitz},
  journal={IEEE Transactions on Signal Processing},
When the assumed data distribution differs from the true distribution, the model is said to be misspecified or mismatched. Model misspecification at some level is an inevitability of engineering practice. While Huber's celebrated work assesses maximum-likelihood (ML) performance under misspecification, no simple theory for bounding parameter estimation exists. The class of parameter bounds emerging from the covariance inequality, or equivalently the minimum norm theorem is revisited. The… CONTINUE READING
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