deriv_qn: Analytic D matrix quantisation noise process

Description Usage Arguments Details Value Author(s) Examples

Description

Analytic D matrix quantisation noise process

Usage

1
deriv_qn(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) = 3*Q[0]^2 / 2*tau^2. Taking the derivative with respect to Q[0]^2 yields:

3/(2*tau^2)

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

0

.

Value

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

Author(s)

JJB

Examples

1
deriv_qn(2^(1:5))

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

Search within the gmwm package
Search all R packages, documentation and source code