qn_to_wv

This function compute the Haar WV of a Quantisation Noise (QN) process

A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) <doi: 10.1080/01621459.2013.799920>. More details can also be found in the paper linked to via the URL below.

St<c3><a9>phane Guerrier

simts

Time Series Analysis Tools

Stéphane Guerrier

James Balamuta

Roberto Molinari

Justin Lee

Lionel Voirol

Yuming Zhang

Wenchao Yang

Nathanael Claussen

Yunxiang Zhang

Christian Gunning

Romain Francois

Ross Ihaka

R Core Team 

qn_to_wv function

<dl><dt>q2</dt>
<dd>A <code>double</code> corresponding to variance of drift</dd>
<dt>tau</dt>
<dd>A <code>vec</code> containing the scales e.g. $2^{\tau}$</dd></dl>

Arguments

The Quantization Noise (QN) process has a Haar Wavelet Variance given by:
$$\nu _j^2\left( {{Q^2}} \right) = \frac{{6{Q^2}}}{{\tau _j^2}}$$

Process Haar Wavelet Variance Formula

Quantisation Noise (QN) to WV — qn_to_wv

<dl>

<dt>q2</dt>
<dd>A <code>double</code> corresponding to variance of drift</dd>


<dt>tau</dt>
<dd>A <code>vec</code> containing the scales e.g. $2^{\tau}$</dd>

</dl>

The Quantization Noise (QN) process has a Haar Wavelet Variance given by:
$$\nu _j^2\left( {{Q^2}} \right) = \frac{{6{Q^2}}}{{\tau _j^2}}$$

qn_to_wv: Quantisation Noise (QN) to WV

Description

Usage

Value

Arguments

Process Haar Wavelet Variance Formula