rotcumvar: Rotated Cumulative Variance
In waveslim: Basic Wavelet Routines for One-, Two-, and Three-Dimensional Signal Processing
rotcumvar R Documentation
Rotated Cumulative Variance
Description
Provides the normalized cumulative sums of squares from a sequence of
coefficients with the diagonal line removed.
Usage
rotcumvar(x)
Arguments
x
vector of coefficients to be cumulatively summed (missing values
excluded)
Details
The rotated cumulative variance, when plotted, provides a qualitative way to
study the time dependence of the variance of a series. If the variance is
stationary over time, then only small deviations from zero should be
present. If on the other hand the variance is non-stationary, then large
departures may exist. Formal hypothesis testing may be performed based on
boundary crossings of Brownian bridge processes.
Value
Vector of coefficients that are the sumulative sum of squared input
coefficients.
Author(s)
B. Whitcher
References
Gencay, R., F. Selcuk and B. Whitcher (2001) An
Introduction to Wavelets and Other Filtering Methods in Finance and
Economics, Academic Press.
Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time
Series Analysis, Cambridge University Press.
waveslim documentation built on June 22, 2024, 9:43 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| rotcumvar | R Documentation |
Rotated Cumulative Variance
Description
Provides the normalized cumulative sums of squares from a sequence of coefficients with the diagonal line removed.
Usage
rotcumvar(x)
Arguments
x |
vector of coefficients to be cumulatively summed (missing values excluded) |
Details
The rotated cumulative variance, when plotted, provides a qualitative way to study the time dependence of the variance of a series. If the variance is stationary over time, then only small deviations from zero should be present. If on the other hand the variance is non-stationary, then large departures may exist. Formal hypothesis testing may be performed based on boundary crossings of Brownian bridge processes.
Value
Vector of coefficients that are the sumulative sum of squared input coefficients.
Author(s)
B. Whitcher
References
Gencay, R., F. Selcuk and B. Whitcher (2001) An Introduction to Wavelets and Other Filtering Methods in Finance and Economics, Academic Press.
Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis, Cambridge University Press.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.