dvFBM {dvfBm} | R Documentation |
Robust estimator of the Hurst parameter of a fractional Brownian possibly contaminated by additive outliers and/or an additive noise.
dvFBM(fbm, nma = "i2", M1 = 1, M2 = 5, method = c("ST", "Q", "TM", "B1-ST", "B1-Q", "B1-TM", "B0-ST", "B0-Q", "B0-TM"), par = list(), llplot = FALSE)
fbm |
data |
nma |
name of the filter used for filtering the data. See filt for possible choices. Default is "i2" |
M1 |
Minimum value of the dilatation factor. Default is 1 . |
M2 |
Maximum value of the dilatation factor. Default is 5 . |
method |
Type of the discrete variations method. |
par |
Parameters depending on method . If method is "Q","B0-Q","B1-Q" , a list with two vectors vecp and vecc is needed. If method is "TM","B0-TM","B1-TM" , a list with two real numbers beta1 and beta2 is needed. |
llplot |
If true a plot of log(U_n^{a^m}) against log(m) for m=M_1,...,M_2 is produced. |
An estimate of the Hurst exponent parameter is provided without estimating the scaling coefficient C and σ (parameter related to an additive noise). The standard method ST is based on filtering the data with dilated versions of the initial filter (whose name is nma
). Other methods are improvements. Methods TM and Q are based on trimmed-means and sample quantiles respectively. Methods B0 and B1 exploit the fact that the contamination is a Brownian motion or a Gaussian white noise. Other methods are combinations of the two last classes. See Achard and Coeurjolly (2009) for more details.
Returns the Hurst parameter estimate
J.-F. Coeurjolly
S. Achard and J.-F. Coeurjolly (2009). Discrete variations of the fractional Brownian in the presence of outliers and an additive noise. Submitted
n<-10000;H<-.8 ## no z<-perturbFBM(n,H,type="no",plot=FALSE) dvFBM(z,method="ST") dvFBM(z,method="TM",par=list(beta1=.1,beta2=.1)) dvFBM(z,method="B0-Q",par=list(vecp=.5,vecc=1)) dvFBM(z,method="B1-ST") ## AO z<-perturbFBM(n,H,type="AO",SNR=-20,plot=FALSE) dvFBM(z,nma="d4",method="ST") dvFBM(z,nma="d4",method="TM",par=list(beta1=.1,beta2=.1)) ## B0 z<-perturbFBM(n,H,type="B0",SNR=0,plot=FALSE) dvFBM(z,M2=10,method="ST") dvFBM(z,M2=10,method="B0-ST") ## B1 z<-perturbFBM(n,H,type="B1",SNR=0,plot=FALSE) dvFBM(z,method="ST") dvFBM(z,method="B1-ST")