estimate_bandwidths_mean: Perform an estimation of the bandwidth for the estimation of...

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

Perform an estimation of the bandwidth for the estimation of the mean.

Description

This function performs an estimation of the bandwidth to be used in the Nadaraya-Watson estimator.

Usage

estimate_bandwidths_mean(
  curves,
  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_param	Vector (default = c(0.25, 0.5, 0.75)), the 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 points in the neighborhood to keep the curve in the estimation.
kernel_name	String (default = 'epanechnikov'), the kernel used for the estimation: epanechnikov uniform biweight

Value

Dataframe, with elements:

• sigma Estimation of the standard deviation of the noise

• variance Estimation of the variance of the process

• hursts Estimation of H_0

• constants Estimation of L_0

• bandwidths Estimation of the bandwidth

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.