On the Generation of Random Multivariate Data
Identificadores
URI: http://hdl.handle.net/10481/55290Metadatos
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Camacho Páez, JoséMateria
Multivariate data Simulation ADICOV MEDA toolbox Método Montecarlo
Fecha
2017-01Resumen
The simulation of multivariate data is often necessary for assessing the performance
of multivariate analysis techniques. The random generation of multivariate data
when the covariance matrix is completely or partly specified is solved by different
methods, from the Cholesky decomposition to some recent alternatives. However,
many times the covariance matrix has to be generated also at random, so that
the data simulation spans different situations from highly correlated to uncorrelated data. This is the case when assessing a new multivariate analysis technique
in Montercarlo experiments. In this paper, we introduce a new algorithm for the
generation of random data from covariance matrices of random structure, where
the user only decides the data dimension and the level of correlation. We will illustrate the application of this algorithm in several relevant problems in multivariate
analysis, namely the selection of the number of Principal Components in Principal Component Analysis, the evaluation of the performance of sparse Partial Least
Squares and the calibration of Multivariate Statistical Process Control systems. The
algorithm is available as part of the MEDA Toolbox v1.1 1