Incremental modelling for compositional data streams

Abstract : Incremental modelling of data streams is of great practical importance, as shown by its applications in advertising and financial data analysis. We propose two incremental covariance matrix decomposition methods for a compositional data type. The first method, exact incremental covariance decomposition of compositional data (C-EICD), gives an exact decomposition result. The second method, covariance-free incremental covariance decomposition of compositional data (C-CICD), is an approximate algorithm that can efficiently compute high-dimensional cases. Based on these two methods, many frequently used compositional statistical models can be incrementally calculated. We take multiple linear regression and principal component analysis as examples to illustrate the utility of the proposed methods via extensive simulation studies.
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https://hal-cnam.archives-ouvertes.fr/hal-02470030
Contributor : Philippe Rigaux <>
Submitted on : Thursday, February 6, 2020 - 11:36:00 PM
Last modification on : Saturday, February 8, 2020 - 1:27:40 AM

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Yuan Wei, Huiwen Wang, Shanshan Wang, Gilbert Saporta. Incremental modelling for compositional data streams. Communications in Statistics - Simulation and Computation, Taylor & Francis, 2018, 48 (8), pp.2229-2243. ⟨10.1080/03610918.2018.1455870⟩. ⟨hal-02470030⟩

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