I want to select a bestfit bivariate Archimedean copula out of 3 to a dataset. I also want to calculate its parameter.for all this which i should use copulafit or copulaparam

2 views (last 30 days)
I want to select a best-fit bivariate Archimedean copula to a dataset. which function to use? I also want to calculate its parameter & for this which i should use copulafit or copulaparam? what is the diffrence between these 2 functions for calculating the parameter?

Accepted Answer

Debraj Maji
Debraj Maji on 25 Nov 2023
Edited: Debraj Maji on 25 Nov 2023
I understand that you are trying to fit a Bivariate Archimedean Copula to an existing dataset and also find the relevant parameters. In MATLAB, both 'copulafit' and 'copulaparam' functions are used for working with copulas, but they serve different purposes.
The function 'copulafit' is used when data is present and you want to fit a copula to that data. This function estimates the parameters of a copula to best fit that given data, and it automatically estimates the copula parameters based on the input data.
The 'copulaparam' is used when you want to get the parameter values of a copula without fitting it to data. This function allows you to specify the data distribution parameters directly, provided that you already know the copula family and want to work with specific parameter values.
In summary, if data is present and you want to estimate the parameters that best fit that particular data, you would use 'copulafit'. If the correlation parameters are already known or you want to specify them directly, 'copulaparam' is a better choice.
For more information on the usage of 'copulaparam' and 'copulafit' you can refer to the following documentation: https://in.mathworks.com/help/stats/copula-distributions-and-correlated-samples.html?s_tid=CRUX_lftnav
I hope this resolves your query.
With regards,
Debraj.

More Answers (0)

Tags

Products


Release

R2023b

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!