estimate_sigma_list: Perform an estimation of the noise given a list of t_0.

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

Description

This function performs an estimation of the standard deviation of the noise at different times.

Usage

estimate_sigma_list(data, t0_list, k0_list)

Arguments

data	A list, where each element represents a curve. Each curve have to be defined as list with two entries: $t The sampling points $x The observed points
t0_list	A vector of numeric, the sampling points at which we estimate H0. We will consider the 8k0 - 7 nearest points of t_0 for the estimation of H_0 when σ is unknown.
k0_list	A vector of numeric, the number of neighbors of t_0 to consider. Should be set as k0 = M * exp(-(log(log(M))**2)) . We can set a different k_0, but in order to use the same for each t_0, just put a unique numeric.

Value

A list, an estimation of sigma at different t_0.

Examples

df <- generate_fractional_brownian(N = 1000, M = 300, H = 0.5, sigma = 0.05)
estimate_sigma_list(df, t0_list = c(0.25, 0.5, 0.75), k0 = 14)

StevenGolovkine/SmoothCurves documentation built on Nov. 14, 2021, 1:12 p.m.