Conditional prediction intervals for linear regression

被引：5

作者：

McCullagh, Peter ^{[1
]}

Vovk, Vladimir ^{[2
]}

Nouretdinov, Ilia ^{[2
]}

Devetyarov, Dmitry ^{[2
]}

Gammerman, Alex ^{[2
]}

机构：

[1] Univ Chicago, Dept Stat, 5734 Univ Ave, Chicago, IL 60637 USA

[2] Royal Holloway Univ London, Dept Comp Sci, Comp Learning Res Ctr, Egham TW20 0EX, Surrey, England

来源：

EIGHTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, PROCEEDINGS | 2009年

基金：

英国工程与自然科学研究理事会; 美国国家科学基金会;

关键词：

D O I：

10.1109/ICMLA.2009.115

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We construct prediction intervals for the linear regression model with IID errors with a known distribution, not necessarily Gaussian. The coverage probability of our prediction intervals is equal to the nominal confidence level not only unconditionally but also conditionally given a natural a-algebra of invariant events. This implies, in particular, the perfect calibration of our prediction intervals in the online mode of prediction.

引用

页码：131 / +

页数：2

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