Bivariate Smoothing Splines

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  • Thread starter Joe Prendergast
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In summary, bivariate smoothing splines are a statistical technique used to fit a smooth surface to data points in two dimensions. They extend univariate smoothing splines to handle two variables, allowing for the modeling of complex relationships between them. The method involves minimizing a penalized residual sum of squares, balancing the fit to the data with the smoothness of the surface. Bivariate smoothing splines are particularly useful in applications such as spatial data analysis, surface interpolation, and any scenario where understanding the interaction between two variables is essential.
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Joe Prendergast
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Does anyone know of a bivariate smoothing spline package that lets you set your own loss function? All of the public domain software I've been able to find (e.g., SCIPY) appears to minimize the sum of squared errors. For example, I'd like to set the spline coefficients to maximize the log-likelihood of a Cauchy distribution of the errors.
 
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  • #2
You could just use a black box optimizer and set the problem up yourself. Depending on how many knots you have...
 

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