HwdS: Compute the non-decimated Haar wavelet transform without...

In locits: Test of Stationarity and Localized Autocovariance

Compute the non-decimated Haar wavelet transform without using periodic boundary conditions.

Description

Function uses the filter function to achieve its aims.

Usage

HwdS(x)

Arguments

x	A vector of dyadic length that you wish to transform.

Details

The regular wd function that can compute the non-decimated transform uses different kinds of boundary conditions, which can result in coefficients being used multiply for consideration in a test of stationarity, and distort results. This function only computes Haar coefficients on the data it can, without wrapround.

Value

An object of class wd which contains the nondecimated Haar transform of the input series, x without periodic boundary conditions (nor interval, nor reflection).

Author(s)

Guy Nason.

References

Nason, G.P. (2013) A test for second-order stationarity and approximate confidence intervals for localized autocovariances for locally stationary time series. J. R. Statist. Soc. B, 75, 879-904. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/rssb.12015")}

See Also

ewspecHaarNonPer, getridofendNA

Examples

#
# Apply Haar transform to Gaussian data
#
HwdS(rnorm(32))
#Class 'wd' : Discrete Wavelet Transform Object:
#       ~~  : List with 8 components with names
#              C D nlevels fl.dbase filter type bc date 
#
#$C and $D are LONG coefficient vectors
#
#Created on : Tue Jul 17 15:14:59 2012 
#Type of decomposition:  station 
#
#summary(.):
#----------
#Levels:  5 
#Length of original:  32 
#Filter was:  Haar wavelet 
#Boundary handling:  periodic 
#Transform type:  station 
#Date:  Tue Jul 17 15:14:59 2012 

locits documentation built on Sept. 8, 2023, 5:07 p.m.