Timeline for Copula from small samples
Current License: CC BY-SA 4.0
4 events
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| Sep 29, 2020 at 20:18 | comment | added | Richard Hardy | @develarist, tough questions. I cannot really comment on Monte Carlo integration; I would need some more details on how exactly that is defined and implemented. Regarding alternatives to copula models in small samples, we would be looking for something less flexible to avoid overfitting that is of particular concern. A multivariate $t$-distribution could perhaps be considered, with the covariance matrix estimated using some form of regularization, e.g. Bayesian (frequentist would usually require tuning the regularization intensity, and that would again be prone to overfitting). | |
| Sep 29, 2020 at 19:56 | comment | added | develarist | Thanks for confirming parametric copula lend themselves better to small sample data. If the parametric copula still shows itself to be inaccurate, however, how else can joint distributions and their dependence structure be modeled, without copula altogether? Or if sticking with copula, for the simple lack of an alternative, would the calculation (approximation) of the copula density by Monte Carlo integration improve accuracy in small sample settings? | |
| Sep 10, 2020 at 16:46 | vote | accept | develarist | ||
| Sep 10, 2020 at 16:37 | history | answered | Richard Hardy | CC BY-SA 4.0 |