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Communication dans un congrès

Computation time/accuracy trade-off and linear regression

Christophe Biernacki 1, 2 Maxime Brunin 2 Alain Celisse 2, 1, 3
2 MODAL - MOdel for Data Analysis and Learning
Inria Lille - Nord Europe, LPP - Laboratoire Paul Painlevé - UMR 8524, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille, Université de Lille, Sciences et Technologies
Abstract : Most estimates practically arise from algorithmic processes aiming at optimizing some standard, but usually only asymptotically relevant, criteria. Thus, the quality of the resulting estimate is a function of both the iteration number and also the involved sample size. An important question is to design accurate estimates while saving computation time, and we address it in the simplified context of linear regression here. Fixing the sample size, we focus on estimating an early stopping time of a gradient descent estimation process aiming at maximizing the likelihood. It appears that the accuracy gain of such a stopping time increases with the number of covariates, indicating potential interest of the method in real situations involving many covariates.
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Communication dans un congrès
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Contributeur : Christophe Biernacki <>
Soumis le : jeudi 22 décembre 2016 - 15:06:14
Dernière modification le : vendredi 27 novembre 2020 - 14:18:02


  • HAL Id : hal-01420659, version 1



Christophe Biernacki, Maxime Brunin, Alain Celisse. Computation time/accuracy trade-off and linear regression. 9th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2016, ERCIM 2016), Dec 2016, Séville, Spain. ⟨hal-01420659⟩



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