mean_ll: Perform an estimation of the mean with local linear...

In StevenGolovkine/funestim: Nonparametric Kernel Smoothing Methods for Functional Data

Perform an estimation of the mean with local linear smoothers.

Description

This function performs the estimation of the mean of a set of curves using local linear smoothers where the bandwidth is estimated using the methodology from Golovkine et al. (2021).

Usage

mean_ll(
  curves,
  grid = seq(0, 1, length.out = 101),
  grid_param = c(0.25, 0.5, 0.75),
  grid_bandwidth = NULL,
  delta_f = NULL,
  n_obs_min = 2,
  kernel_name = "epanechnikov"
)

Arguments

curves	List, where each element represents a curve. Each curve have to be defined as a list with two entries: $t Sampling points $x Observed points.
grid	Vector (default = seq(0, 1, length.out = 101)), sampling points at which estimate the curves.
grid_param	Vector (default = c(0.25, 0.5, 0.75)), sampling points at which we estimate the parameters.
grid_bandwidth	Vector (default = NULL), grid of bandwidths.
delta_f	Function (default = NULL), function to determine the delta.
n_obs_min	Integer (default = 2), minimum number of observation for the smoothing.
kernel_name	String (default = 'epanechnikov'), the kernel used for the estimation: epanechnikov uniform biweight

Value

List of with two entries:

• $parameter Estimated parameters.

• $mu Estimated mean.

References

Golovkine S., Klutchnikoff N., Patilea V. (2021) - Adaptive estimation of irregular mean and covariance functions.

StevenGolovkine/funestim documentation built on June 15, 2022, 3:42 a.m.