Model Mismatch Trade-offs in LMMSE Estimation
This addresses a fundamental issue in statistical estimation for researchers and practitioners, but it is incremental as it builds on existing LMMSE frameworks.
The paper tackles the problem of linear minimum mean squared error (LMMSE) estimation with model mismatch, where the assumed model order is smaller than the true system, and analyzes how the mean squared error (MSE) depends on sample size and model parameters, finding that insufficient samples can prevent performance improvements from more samples or increased model complexity.
We consider a linear minimum mean squared error (LMMSE) estimation framework with model mismatch where the assumed model order is smaller than that of the underlying linear system which generates the data used in the estimation process. By modelling the regressors of the underlying system as random variables, we analyze the average behaviour of the mean squared error (MSE). Our results quantify how the MSE depends on the interplay between the number of samples and the number of parameters in the underlying system and in the assumed model. In particular, if the number of samples is not sufficiently large, neither increasing the number of samples nor the assumed model complexity is sufficient to guarantee a performance improvement.