ftsa (version 1.2)

median.fts: Median functions for functional time series

Description

Computes median of functional time series at each variable.

Usage

median.fts(x, method = c("hossjercroux", "coordinate", "FM", "mode", 
 "RP", "RPD"), ...)

Value

A list containing x = variables and y = median rates.

Details

If method = "coordinate", it computes a coordinate-wise median. If method = "hossjercroux", it computes the L1-median using the Hossjer-Croux algorithm. If method = "FM", it computes the median of trimmed functional data ordered by the functional depth of Fraiman and Muniz (2001). If method = "mode", it computes the median of trimmed functional data ordered by $h$-modal functional depth. If method = "RP", it computes the median of trimmed functional data ordered by random projection depth. If method = "RPD", it computes the median 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, var.fts, sd.fts, quantile.fts

Examples

median(x = ausmale, method = "hossjercroux")
median(x = ausmale, method = "coordinate")
median(x = ausmale, method = "FM")
median(x = ausmale, method = "mode")
median(x = ausmale, method = "RP")
median(x = ausmale, method = "RPD")