Nonparametric predictive distributions based on conformal prediction. / Vovk, Vladimir; Shen, Jieli; Manokhin, Valery; Xie, Minge.

In: Machine Learning, Vol. 108, No. 3, 03.2019, p. 445-474.

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Nonparametric predictive distributions based on conformal prediction. / Vovk, Vladimir; Shen, Jieli; Manokhin, Valery; Xie, Minge.

In: Machine Learning, Vol. 108, No. 3, 03.2019, p. 445-474.

Research output: Contribution to journalArticle

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Vovk, Vladimir ; Shen, Jieli ; Manokhin, Valery ; Xie, Minge. / Nonparametric predictive distributions based on conformal prediction. In: Machine Learning. 2019 ; Vol. 108, No. 3. pp. 445-474.

BibTeX

@article{972f67dc5e1c4cdb9babe4e223c4327e,
title = "Nonparametric predictive distributions based on conformal prediction",
abstract = "This paper applies conformal prediction to derive predictive distributions that are valid under a nonparametric assumption. Namely, we introduce and explore predictive distribution functions that always satisfy a natural property of validity in terms of guaranteed coverage for IID observations. The focus is on a prediction algorithm that we call the Least Squares Prediction Machine (LSPM). The LSPM generalizes the classical Dempster-Hill predictive distributions to nonparametric regression problems. If the standard parametric assumptions for Least Squares linear regression hold, the LSPM is as efficient as the Dempster-Hill procedure, in a natural sense. And if those parametric assumptions fail, the LSPM is still valid, provided the observations are IID.",
keywords = "Conformal prediction, Least Squares , Predictive distributions, Regression, Nonparametric regression",
author = "Vladimir Vovk and Jieli Shen and Valery Manokhin and Minge Xie",
year = "2019",
month = "3",
doi = "10.1007/s10994-018-5755-8",
language = "English",
volume = "108",
pages = "445--474",
journal = "Machine Learning",
issn = "0885-6125",
publisher = "Springer Netherlands",
number = "3",

}

RIS

TY - JOUR

T1 - Nonparametric predictive distributions based on conformal prediction

AU - Vovk, Vladimir

AU - Shen, Jieli

AU - Manokhin, Valery

AU - Xie, Minge

PY - 2019/3

Y1 - 2019/3

N2 - This paper applies conformal prediction to derive predictive distributions that are valid under a nonparametric assumption. Namely, we introduce and explore predictive distribution functions that always satisfy a natural property of validity in terms of guaranteed coverage for IID observations. The focus is on a prediction algorithm that we call the Least Squares Prediction Machine (LSPM). The LSPM generalizes the classical Dempster-Hill predictive distributions to nonparametric regression problems. If the standard parametric assumptions for Least Squares linear regression hold, the LSPM is as efficient as the Dempster-Hill procedure, in a natural sense. And if those parametric assumptions fail, the LSPM is still valid, provided the observations are IID.

AB - This paper applies conformal prediction to derive predictive distributions that are valid under a nonparametric assumption. Namely, we introduce and explore predictive distribution functions that always satisfy a natural property of validity in terms of guaranteed coverage for IID observations. The focus is on a prediction algorithm that we call the Least Squares Prediction Machine (LSPM). The LSPM generalizes the classical Dempster-Hill predictive distributions to nonparametric regression problems. If the standard parametric assumptions for Least Squares linear regression hold, the LSPM is as efficient as the Dempster-Hill procedure, in a natural sense. And if those parametric assumptions fail, the LSPM is still valid, provided the observations are IID.

KW - Conformal prediction

KW - Least Squares

KW - Predictive distributions

KW - Regression

KW - Nonparametric regression

U2 - 10.1007/s10994-018-5755-8

DO - 10.1007/s10994-018-5755-8

M3 - Article

VL - 108

SP - 445

EP - 474

JO - Machine Learning

JF - Machine Learning

SN - 0885-6125

IS - 3

ER -