cdt15
Is the bootstrap method implemented here a multiscale one?
When using bootstrap to evaluate the causal structure given by LiNGAM, multiscale bootstrap is said to be better. Is the bootstrap method already prepared here a multiscale one?
Hi, multiscale bootstrap is not implemented in this package for LiNGAM.
Though not specialized to LiNGAM, in general cases, R package for multi scale bootstrap is available: https://github.com/shimo-lab/scaleboot
Hi, thank you for your reply and introducing R package for multi scale bootstrap. Can I somehow use this package to evaluate the result LiNGAM gives?
Yes. Basically, it would be something like computing bootstrap probabilities with different numbers of bootstrap resampling and then giving them to the R code.
Thanks for your reply. Does "computing bootstrap probabilities with different numbers of bootstrap resampling" mean execute the bootstrap() method with different number of n_sampling like the following? 1)model.bootstrap(X, n_sampling=100) 2) model.bootstrap(X, n_sampling=200) 3) model.bootstrap(X, n_sampling=300) 4)give the results1〜3 to R code
Yes, something like that, though I don't know the details of the R package very much.
Thank you very much for your kind replies. I'll try using the package. By the way, I have one more question. I believe there are two options when selecting the model, 'pwling' or 'kernel'. How should I use these options differently?
Basically, pwling would be the first choice since it is faster to compute.
kernel is a kind of nonparametric estimator of independence. pwling makes some distributional assumption like super-Gaussian distributions. See the details for these references: https://www.jmlr.org/papers/v14/hyvarinen13a.html for pwling, and https://www.jmlr.org/papers/v3/bach02a.html for kernel.
Thanks for your reply and sharing the URLs. I believed I should use the two methods separately depending on the features of the data used for input like skewness or kurtosis. Is this not that kind of thing?
Yeah, you can try both of them depending on the nature of the distributions of variables.
Thank you. I tried multiscale bootstrapping using the R package, and I got some results, but in the example, they use log-likelihood values as input. Is it appropriate to use bootstrap probabilities as input?". I'm also wondering how many times I should do bootstrap before using this package.