Functional robust Frobenius norm
Computes the functional robust Frobenius norm.
frobenius_norm_funct_robust(m, PM, prob)
m |
Data matrix with the residuals. This matrix has the same dimensions as the original data matrix. |
PM |
Penalty matrix obtained with |
prob |
Probability with values in [0,1]. |
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.
Real number.
Irene Epifanio
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
library(fda) mat <- matrix(1:9, nrow = 3) fbasis <- create.fourier.basis(rangeval = c(1, 32), nbasis = 3) PM <- eval.penalty(fbasis) frobenius_norm_funct_robust(mat, PM, 0.8)
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