smooth_curves_mean: Perform a non-parametric smoothing of a set of curves for...

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

Perform a non-parametric smoothing of a set of curves for mean estimation.

Description

This function performs a non-parametric smoothing of a set of curves using the Nadaraya-Watson estimator.

Usage

smooth_curves_mean(
  curves,
  grid = NULL,
  grid_param = c(0.25, 0.5, 0.75),
  grid_bandwidth = NULL,
  delta_f = NULL,
  kernel_name = "epanechnikov",
  n_obs_min = 2
)

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 = NULL), sampling points at which estimate the curves. If NULL, the sampling points for the estimation are the same than the observed ones.
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.
kernel_name	String (default = 'epanechnikov'), the kernel used for the estimation: epanechnikov uniform biweight
n_obs_min	Integer (default = 2), minimum number of observation for the smoothing.

Value

A list, which contains two elements. The first one is a list which contains the estimated parameters:

• sigma Estimation of the standard deviation of the noise.

• variance Estimation of the variance of the process.

• H0 Estimation of H_0.

• L0 Estimation of L_0.

• bandwidth Estimation of the bandwidth.

The second one is another list which contains the estimation of the curves:

• $t Sampling points.

• $x Estimated points.

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.