Minimum complexity regression estimation with weakly dependent observations

@article{Modha1996MinimumCR,
  title={Minimum complexity regression estimation with weakly dependent observations},
  author={Dharmendra S. Modha and Elias Masry},
  journal={IEEE Trans. Information Theory},
  year={1996},
  volume={42},
  pages={2133-2145}
}
The minimum complexity regression estimation framework, due to Andrew Barron, is a general data-driven methodology for estimating a regression function from a given list of parametric models using independent and identically distributed (i.i.d.) observations. We extend Barron’s regression estimationframework tom-dependent observations and to stronglymixing observations. In particular, we propose abstractminimumcomplexity regression estimators for dependent observations, which may be adapted to… CONTINUE READING

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