ftsa (version 3.2)

sd.fts: Standard deviation functions for functional time series

Description

Computes standard deviation of functional time series at each variable.

Usage

## S3 method for class 'fts':
sd(x, method = c("coordinate", "FM", "mode", "RP", "RPD"), 
 trim = 0.25,...)

Value

A list containing x = variables and y = standard deviation rates.

Details

If method = "coordinate", it computes coordinate-wise standard deviation functions. If method = "FM", it computes the standard deviation functions of trimmed functional data ordered by the functional depth of Fraiman and Muniz (2001). If method = "mode", it computes the standard deviation functions of trimmed functional data ordered by $h$-modal functional depth. If method = "RP", it computes the standard deviation functions of trimmed functional data ordered by random projection depth. If method = "RPD", it computes the standard deviation functions of trimmed functional data ordered by random projection derivative depth.

References

O. Hossjer and C. Croux (1995) "Generalized univariate signed rank statistics for testing and estimating a multivariate location parameter", Nonparametric Statistics, 4(3), 293-308. A. Cuevas and M. Febrero and R. Fraiman (2006) "On the use of bootstrap for estimating functions with functional data", Computational Statistics & Data Analysis, 51(2), 1063-1074. M. Febrero and P. Galeano and W. Gonzalez-Manteiga (2008) "Outlier detection in functional data by depth measures, with application to identify abnormal NOx levels", Environmetrics, 19(4), 331-345.

See Also

mean.fts, median.fts, var.fts, quantile.fts

Examples

sd.fts(x = ElNino, method = "coordinate")
sd.fts(x = ElNino, method = "FM")
sd.fts(x = ElNino, method = "mode")
sd.fts(x = ElNino, method = "RP")
sd.fts(x = ElNino, method = "RPD")