Learning Local Error Bars for Nonlinear Regression

NeurIPS 1994 pp. 489-496

/neurips/1994/nix1994neurips-learning/

Abstract

We present a new method for obtaining local error bars for nonlinear regression, i.e., estimates of the confidence in predicted values that de(cid:173) pend on the input. We approach this problem by applying a maximum(cid:173) likelihood framework to an assumed distribution of errors. We demon(cid:173) strate our method first on computer-generated data with locally varying, normally distributed target noise. We then apply it to laser data from the Santa Fe Time Series Competition where the underlying system noise is known quantization error and the error bars give local estimates of model misspecification. In both cases, the method also provides a weighted(cid:173) regression effect that improves generalization performance.

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Text

Nix and Weigend. "Learning Local Error Bars for Nonlinear Regression." Neural Information Processing Systems, 1994.

Markdown

[Nix and Weigend. "Learning Local Error Bars for Nonlinear Regression." Neural Information Processing Systems, 1994.](https://mlanthology.org/neurips/1994/nix1994neurips-learning/)

BibTeX

@inproceedings{nix1994neurips-learning,
  title     = {{Learning Local Error Bars for Nonlinear Regression}},
  author    = {Nix, David A. and Weigend, Andreas S.},
  booktitle = {Neural Information Processing Systems},
  year      = {1994},
  pages     = {489-496},
  url       = {https://mlanthology.org/neurips/1994/nix1994neurips-learning/}
}