frobenius_norm_funct_multiv_robust: Functional multivariate robust Frobenius norm

In adamethods: Archetypoid Algorithms and Anomaly Detection

Description

Computes the functional multivariate robust Frobenius norm.

Usage

frobenius_norm_funct_multiv_robust(m, PM, prob, nbasis, nvars)

Arguments

m	Data matrix with the residuals. This matrix has the same dimensions as the original data matrix.
PM	Penalty matrix obtained with eval.penalty.
prob	Probability with values in [0,1].
nbasis	Number of basis.
nvars	Number of variables.

Details

Residuals are vectors. If there are p variables (columns), for every observation there is a residual that there is a p-dimensional vector. If there are n observations, the residuals are an n times p matrix.

Value

Real number.

Author(s)

Irene Epifanio

References

Moliner, J. and Epifanio, I., Robust multivariate and functional archetypal analysis with application to financial time series analysis, 2019. Physica A: Statistical Mechanics and its Applications 519, 195-208. https://doi.org/10.1016/j.physa.2018.12.036

Examples

mat <- matrix(1:400, ncol = 20)
PM <- matrix(1:100, ncol = 10)
frobenius_norm_funct_multiv_robust(mat, PM, 0.8, 10, 2)
                 

adamethods documentation built on Aug. 4, 2020, 5:08 p.m.