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Independent block identification in multivariate time series
Author(s) -
Leonardi Florencia,
LopezRosenfeld Matías,
Rodriguez Daniela,
Severino Magno T. F.,
Sued Mariela
Publication year - 2021
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12553
Subject(s) - mathematics , estimator , series (stratigraphy) , consistency (knowledge bases) , independence (probability theory) , statistics , mathematical optimization , algorithm , discrete mathematics , paleontology , biology
In this‐30 work we propose a model selection criterion to estimate the points of independence of a random vector, producing a decomposition of the vector distribution function into independent blocks. The method, based on a general estimator of the distribution function, can be applied for discrete or continuous random vectors, and for i.i.d. data or dependent time series. We prove the consistency of the approach under general conditions on the estimator of the distribution function and we show that the consistency holds for i.i.d. data and discrete time series with mixing conditions. We also propose an efficient algorithm to approximate the estimator and show the performance of the method on simulated data. We apply the method in a real dataset to estimate the distribution of the flow over several locations on a river, observed at different time points.