fbootstrap: Bootstrap independent and identically distributed functional...

In ftsa: Functional Time Series Analysis

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

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.

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.

Author(s)

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)

ftsa documentation built on May 29, 2024, 2:47 a.m.