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Statistical properties of sketching algorithms.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Ahfock, DC 
Astle, WJ 

Abstract

Sketching is a probabilistic data compression technique that has been largely developed by the computer science community. Numerical operations on big datasets can be intolerably slow; sketching algorithms address this issue by generating a smaller surrogate dataset. Typically, inference proceeds on the compressed dataset. Sketching algorithms generally use random projections to compress the original dataset, and this stochastic generation process makes them amenable to statistical analysis. We argue that the sketched data can be modelled as a random sample, thus placing this family of data compression methods firmly within an inferential framework. In particular, we focus on the Gaussian, Hadamard and Clarkson-Woodruff sketches and their use in single-pass sketching algorithms for linear regression with huge samples. We explore the statistical properties of sketched regression algorithms and derive new distributional results for a large class of sketching estimators. A key result is a conditional central limit theorem for data-oblivious sketches. An important finding is that the best choice of sketching algorithm in terms of mean squared error is related to the signal-to-noise ratio in the source dataset. Finally, we demonstrate the theory and the limits of its applicability on two datasets.

Description

Keywords

Computational efficiency, Random projection, Randomized numerical linear algebra, Sketching

Journal Title

Biometrika

Conference Name

Journal ISSN

0006-3444
1464-3510

Volume Title

108

Publisher

Oxford University Press (OUP)
Sponsorship
MRC (Unknown)
This research was conducted using the UK Biobank resource. Richardson was supported by the UKRI Medical Research Council and the Alan Turing Institute. Astle was supported by NHS Blood and Transplant and the National Institute for Health Research Blood and Transplant Research Unit.