deriv_rw: Analytic D matrix random walk process

In gmwm: Generalized Method of Wavelet Moments

Description

Analytic D matrix random walk process

Usage

deriv_rw(tau)

Arguments

tau	A vec that contains the scales to be processed (e.g. 2^(1:J))

Details

The haar wavelet variance is given as nu^2(tau) = (2*tau^2+1)*gamma^2 / (24*tau). Taking the first derivative with respect to gamma_0^2 yields:

(2*tau^2+1) / (24*tau)

The second derivative derivative with respect to gamma[0]^2 is then:

0

.

Value

A matrix with the first column containing the partial derivative with respect to gamma[0]^2.

Author(s)

JJB

Examples

deriv_rw(2^(1:5))

gmwm documentation built on April 14, 2017, 4:38 p.m.