Sequential linear regression with online standardized data

Kévin Duarte, Jean-Marie Monnez and Eliane Albuisson

PLOS ONE, 2018, vol. 13, issue 1, 1-27

Abstract: The present study addresses the problem of sequential least square multidimensional linear regression, particularly in the case of a data stream, using a stochastic approximation process. To avoid the phenomenon of numerical explosion which can be encountered and to reduce the computing time in order to take into account a maximum of arriving data, we propose using a process with online standardized data instead of raw data and the use of several observations per step or all observations until the current step. Herein, we define and study the almost sure convergence of three processes with online standardized data: a classical process with a variable step-size and use of a varying number of observations per step, an averaged process with a constant step-size and use of a varying number of observations per step, and a process with a variable or constant step-size and use of all observations until the current step. Their convergence is obtained under more general assumptions than classical ones. These processes are compared to classical processes on 11 datasets for a fixed total number of observations used and thereafter for a fixed processing time. Analyses indicate that the third-defined process typically yields the best results.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0191186

DOI: 10.1371/journal.pone.0191186

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