Dear All; I have used fbmSim to simulate a fbm sequence, however, when I tried to estimate the Hurst effect, none of the nine procedures gave me an answer close enough to the real value, which is 0.5 (n=1000). So, would you please advice, 1. which is the best method to estimate the H among the 9 mehods, R/S, higuchi or Whittle? 2. how to choose the levels (default=50), minnpts, cutoff values or if there is any other issues to consider? Would you please send your code if you can get the right estimate for the attached dataset? 3. I also simulated multiple sequences, but some of the estimated results have H>1, how would you explain this, and how to correct it. 4. if I have a sample size of 30, what are your suggestions for estimating the hurst effect. I really appreciate your help, and thanks in advance. Sincerely; Qing list(c(0, 0.0602883614054335, 0.0792551804556156, 0.109563140496959, 0.125726475390261, 0.101877497256596, 0.0571926667502657, 0.00262554012667387, -0.0651825041744684, -0.0696503406796901, -0.0688337926741136, -0.0361989921035026, -0.0496524166395568, -0.0718218507093852, -0.109583486993751, -0.0451891749832022, -0.0260755094163026, -0.0201228029445018, -0.08818052945358, -0.0285680272562049, -0.032752629105123, -0.00199553309054604, 0.00508312076775159, -0.0382257524810562, -0.0398084284989033, -0.0502088451482971, -0.0502183047870017, -0.0174725900800023, -0.031457396692228, -0.0497298422601627, -0.0499422794745919, -0.0361646489128442, 0.0266653302426084, -0.00219111075589404, 0.0200305010061950, -0.054431205284715, -0.00789973517059908, -0.0085425214867059, -0.0541809440614641, -0.110006586793787, -0.127781398474619, -0.149159749479771, -0.0888724748560444, -0.0768016585462973, -0.0427333724364433, -0.0562443902614682, -0.062866380084345, -0.0883889349587378, -0.08412713827269, -0.124694662643563, - 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You have applied the Hurst parameter estimating function to the fBm, not the fGn as described in the help page for Long Range Dependence Modelling in fSeries. "The function aggvarFit computes the Hurst exponent from the variance of an aggregated FGN or FARIMA time series process" Trying perFit(diff(x)) (where x is your fBm series) gives an estimate of the Hurst Exponent as 0.53527922 -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of ED Sent: Wednesday, January 10, 2007 8:53 AM To: r-help at stat.math.ethz.ch Subject: [R] Fractional brownian motion Dear All; I have used fbmSim to simulate a fbm sequence, however, when I tried to estimate the Hurst effect, none of the nine procedures gave me an answer close enough to the real value, which is 0.5 (n=1000). So, would you please advice, 1. which is the best method to estimate the H among the 9 mehods, R/S, higuchi or Whittle? 2. how to choose the levels (default=50), minnpts, cutoff values or if there is any other issues to consider? Would you please send your code if you can get the right estimate for the attached dataset? 3. I also simulated multiple sequences, but some of the estimated results have H>1, how would you explain this, and how to correct it. 4. if I have a sample size of 30, what are your suggestions for estimating the hurst effect. I really appreciate your help, and thanks in advance. Sincerely; Qing list(c(0, 0.0602883614054335, 0.0792551804556156, 0.109563140496959, 0.125726475390261, 0.101877497256596, 0.0571926667502657, 0.00262554012667387, -0.0651825041744684, -0.0696503406796901, -0.0688337926741136, -0.0361989921035026, -0.0496524166395568, -0.0718218507093852, -0.109583486993751, -0.0451891749832022, -0.0260755094163026, -0.0201228029445018, -0.08818052945358, -0.0285680272562049, -0.032752629105123, -0.00199553309054604, 0.00508312076775159, -0.0382257524810562, -0.0398084284989033, -0.0502088451482971, -0.0502183047870017, -0.0174725900800023, -0.031457396692228, -0.0497298422601627, -0.0499422794745919, -0.0361646489128442, 0.0266653302426084, -0.00219111075589404, 0.0200305010061950, -0.054431205284715, -0.00789973517059908, -0.0085425214867059, -0.0541809440614641, -0.110006586793787, -0.127781398474619, -0.149159749479771, -0.0888724748560444, -0.0768016585462973, -0.0427333724364433, -0.0562443902614682, -0.062866380084345, -0.0883889349587378, -0.08412713827269, -0.124694662643563, - 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Hi All:
I'm testing a set of data classification algorithms in this paper
(www.stat.wisc.edu/~loh/treeprogs/quest1.7/mach1317.pdf )
I couldn't find such algorithms in R packages:
1. LOG: polytomous logistic regression (there was one in MASS
library: multinom. But after I update MASS library, multinom was lost.)
2. POL: POLYCLASS algorithm. There is a S-Plus package(polyclass
library) for this algorithm, so there should be a corresponding package in
R, but I haven't found it so far.
Any advice is appreciated.
Best,
Feng
1. multinom is is the nnet package 2. There is a polyclass function in package polspline On 10/01/07, Feng Qiu <hellokisas at gmail.com> wrote:> Hi All: > I'm testing a set of data classification algorithms in this paper > (www.stat.wisc.edu/~loh/treeprogs/quest1.7/mach1317.pdf ) > I couldn't find such algorithms in R packages: > 1. LOG: polytomous logistic regression (there was one in MASS > library: multinom. But after I update MASS library, multinom was lost.) > 2. POL: POLYCLASS algorithm. There is a S-Plus package(polyclass > library) for this algorithm, so there should be a corresponding package in > R, but I haven't found it so far. > Any advice is appreciated. > > Best, > > Feng > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >-- ================================David Barron Said Business School University of Oxford Park End Street Oxford OX1 1HP -- ================================David Barron Said Business School University of Oxford Park End Street Oxford OX1 1HP