Incremental modelling for compositional data streams. Issue 8 (14th September 2019)

• Record Type: Journal Article
• Authors: Wei, Yuan; Wang, Huiwen; Wang, Shanshan; Saporta, Gilbert
• Is Part Of: Communications in statistics. Volume 48:Issue 8(2019)
• Page Start: 2229
• Page End: 2243
• Publication Date: 2019-09-14
• Subjects: Compositional data -- Covariance matrix -- Eigen decomposition -- Data stream; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2018.1455870
• Languages: English
• ISSNs: 0361-0918

Abstract

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 principle component analysis as examples to illustrate the utility of the proposed methods via extensive simulation studies.