fbootstrap: Bootstrap independent and identically distributed functional data

Description

Computes bootstrap or smoothed bootstrap samples based on independent and identically distributed functional data.

Usage

fbootstrap(data, estad = func.mean, alpha = 0.05, nb = 200, suav = 0,
 media.dist = FALSE, graph = FALSE, ...)

Arguments

Value

A list containing the following components is returned.estimateEstimate function.max.distMax distance of bootstrap samples.rep.distDistances of bootstrap samples.resamplesBootstrap samples.centerFunctional mean.

References

A. Cuevas and M. Febrero and R. Fraiman (2006), "On the use of the bootstrap for estimating functions with functional data", Computational Statistics and Data Analysis, 51(2), 1063-1074. M. Febrero and P. Galeano and W. Gonzalez-Manteiga (2007) "A functional analysis of NOx levels: location and scale estimation and outlier detection", Computational Statistics, 22(3), 411-427. 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. M. Febrero and P. Galeano and W. Gonzalez-Manteiga (2010) "Measures of influence for the functional linear model with scalar response", Journal of Multivariate Analysis, 101(2), 327-339.

Examples

Run this code

fbootstrap(data = ElNino)