How to Constrain Covariate Coefficients to Positive Values in Nonstationary GEV Models Using extRemes?

Machine Learning and Modeling
1

Hi everyone,

I am working on fitting a nonstationary Generalized Extreme Value (GEV) model to discharge data (q) using the extRemes package in R. My goal is to model the location and scale parameters as linear functions of covariates. Here's my current setup:

mod4965 <- fevd(q,
location.fun = ~ cov1 + cov5 + cov7,
scale.fun = ~ cov1 + cov2 + cov5 + cov7,
type = 'GEV',
method = 'MLE')

In my case, it is physically unreasonable for the covariate coefficients to be negative, so I need to ensure that all estimated coefficients are positive.

Any advice, examples, or suggestions on how to handle this would be greatly appreciated.

Thanks in advance for your help!

2

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