توضیحات
ABSTRACT
The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large datasets, the computation of bootstrap-based quantities can be prohibitively demanding. As an alternative, we present the Bag of Little Bootstraps (BLB), a new procedure which incorporates features of both the bootstrap and subsampling to obtain a robust, computationally ecient means of assessing estimator quality. BLB is well suited to modern parallel and distributed computing architectures and retains the generic applicability, statistical
eciency, and favorable theoretical properties of the bootstrap. We provide the results of an extensive empirical and theoretical investigation of BLB’s behavior, including a study of its statistical correctness, its largescale implementation and performance, selection of hyperparameters, and performance on real data
INTRODUCTION
Assessing the quality of estimates based upon nite data is a task of fundamental importance in data analysis. For example, when estimating a vector of model parameters given a training dataset, it is useful to be able to quantify the uncertainty in that estimate (e.g.,via a con dence region), its bias, and its risk. Such quality assessments provide far more information than a simple point estimate itself and can be used to improve human interpretation of inferential outputs, per- form hypothesis testing, do bias correction, make more ecient use of available resources (e.g., by ceasing to process data when the con dence region is suciently small), perform active learning, and do feature selection, among many more potential uses. Accurate assessment of estimate quality has been a longstanding concern in statistics. A great deal of classical work in this vein has proceeded via asymptotic analysis, which relies on deep study of particular classes of estimators in particular settings (Politiset al., 1999).
Year : 2012
Publisher : Appearing in Proceedings of the 29 th International Confer-ence on Machine Learning
By : Ariel Kleiner , Ameet Talwalkar , Purnamrita Sarkar
File Information : English language / 8 Page / Size : 343 K
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سال : 2012
ناشر : Appearing in Proceedings of the 29 th International Confer-ence on Machine Learning
کاری از : Ariel Kleiner , Ameet Talwalkar , Purnamrita Sarkar
اطلاعات فایل : زبان انگلیسی / 8 صفحه /حجم : 343 K
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