Rate-Optimal Non-Asymptotics for the Quadratic Prediction Error Method
<|reference_start|>Rate-Optimal Non-Asymptotics for the Quadratic Prediction Error Method: We study the quadratic prediction error method -- i.e., nonlinear least squares -- for a class of time-varying parametric predictor models satisfying a certain identifiability condition. While this method is known to asymptotically achieve the optimal rate for a wide range of problems, there have been no non-asymptotic results matching these optimal rates outside of a select few, typically linear, model classes. By leveraging modern tools from learning with dependent data, we provide the first rate-optimal non-asymptotic analysis of this method for our more general setting of nonlinearly parametrized model classes. Moreover, we show that our results can be applied to a particular class of identifiable AutoRegressive Moving Average (ARMA) models, resulting in the first optimal non-asymptotic rates for identification of ARMA models.<|reference_end|>
@article{stamouli2024rate-optimal,
    title={Rate-Optimal Non-Asymptotics for the Quadratic Prediction Error Method},
    author={Charis Stamouli, Ingvar Ziemann, George J. Pappas},
    journal={arXiv preprint arXiv:2404.07937},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07937},
    primaryClass={math.ST cs.LG cs.SY eess.SY stat.ML stat.TH}
}