statistical analysis, probabibility density function, negative log-likelihood values
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good day all,
I had a wave data set of a length of 3000. then i sorted out all the peaks and valleys of this wave. and came out with 75 amplitudes which i put in a matrix:
amp=[0.1646 0.0829 0.1354 0.2488 0.0915 0.1415 0.1646 0.0805 0.1720 0.1878 0.1537 0.0988 0.3110 0.3720 0.1683 0.3829 0.2220 0.0402 0.1841 0.2732 0.1744 0.0829 0.2244 0.1427 0.0976 0.0902 0.2598 0.1671 0.1256 0.4280 0.1695 0.1720 0.1463 0.3720 0.1244 0.0939 0.0817 0.3988 0.0902 0.2268 0.2476 0.1500 0.2012 0.0622 0.0390 0.1573 0.0756 0.2744 0.2122 0.3573 0.1171 0.3378 0.2098 0.1317 0.2683 0.3146 0.1878 0.1537 0.0756 0.1829 0.3122 0.1232 0.1537 0.1902 0.2829 0.1427 0.4634 0.0829 0.1305 0.1476 0.0780 0.0549 0.3061 0.1524 0.2280];
after that, I chose Normal, gamma, lognormal and Weibull distribution function types to obtain their Negative log-likelihood values, which gave me
- paramGaussian=normfit(amp); NgtvLgLkhd(1,1)=normlike(paramGaussian,amp);
paramGamma=gamfit(amp);
NgtvLgLkhd(1,2)=gamlike(paramGamma,amp);
paramLogn=lognfit(amp);
NgtvLgLkhd(1,3)=lognlike(paramLogn,amp);
paramWbl=wblfit(amp);
NgtvLgLkhd(1,4)=wbllike(paramWbl,amp);
NgtvLgLkhd=[-46.9030 -75.8580 -75.2367 -76.2557];*
then, i plotted the histogram of amp and Weibull function fitting curve, the result went very well. So, my quenstion is, did this mean that amp value follow a unique weibull distribution? was it because the
magnitude of negative log-likelihood value? any further validation methods? Thanks. Cheers.
all the best
Will
回答(1 个)
bym
2011-4-5
1 个投票
Not sure about the negative log likelihood, but you could try a non-parametric estimate like Anderson-Darling for further validation
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