ftsa (version 6.4)

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, ...)

Value

A list containing the following components is returned.

estimate

Estimate function.

max.dist

Max distance of bootstrap samples.

rep.dist

Distances of bootstrap samples.

resamples

Bootstrap samples.

center

Functional mean.

Arguments

data

An object of class fds or fts.

estad

Estimate function of interest. Default is to estimate the mean function. Other options are func.mode or func.var.

alpha

Significance level used in the smooth bootstrapping.

nb

Number of bootstrap samples.

suav

Smoothing parameter.

media.dist

Estimate mean function.

graph

Graphical output.

...

Other arguments.

Author

Han Lin Shang

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.

A. Cuevas and M. Febrero and R. Fraiman (2007), "Robust estimation and classification for functional data via projection-based depth notions", Computational Statistics, 22(3), 481-496.

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.

J. A. Cuesta-Albertos and A. Nieto-Reyes (2010) "Functional classification and the random Tukey depth. Practical issues", Combining Soft Computing and Statistical Methods in Data Analysis, Advances in Intelligent and Soft Computing, 77, 123-130.

D. Gervini (2012) "Outlier detection and trimmed estimation in general functional spaces", Statistica Sinica, 22(4), 1639-1660.

H. L. Shang (2015) "Re-sampling techniques for estimating the distribution of descriptive statistics of functional data", Communication in Statistics--Simulation and Computation, 44(3), 614-635.

H. L. Shang (2018) Bootstrap methods for stationary functional time series, Statistics and Computing, 28(1), 1-10.

See Also

pcscorebootstrapdata

Examples

# Bootstrapping the distribution of a summary statistics of functional data.
fbootstrap(data = ElNino_ERSST_region_1and2)