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[WWW18] Incremental modelling for compositional data streams
Revue Internationale avec comité de lecture : Journal Communications in Statistics - Simulation and Computation, pp. 1-15, 2018, (doi:10.1080/03610918.2018.1455870)Mots clés: Compositional data, Covariance matrix, Eigen decomposition, Data stream
Résumé:
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.
BibTeX
| @article { | |||
| WWW18, | |||
| title | = | "{Incremental modelling for compositional data streams}", | |
| author | = | "Y. Wei and H. Wang and S. Wang and G. Saporta", | |
| journal | = | "Communications in Statistics - Simulation and Computation", | |
| year | = | 2018, | |
| pages | = | "1-15", | |
| doi | = | "10.1080/03610918.2018.1455870", | |
| } | |||
