Fitting very flexible models: Linear regression with large numbers of parameters. (arXiv:2101.07256v1 [physics.data-an])
Fitting very flexible models: Linear regression with large numbers of parameters. (arXiv:2101.07256v1 [physics.data-an]) <a href="http://arxiv.org/find/physics/1/au:+Hogg_D/0/1/0/all/0/1">David W. Hogg</a> (NYU), <a href="http://arxiv.org/find/physics/1/au:+Villar_S/0/1/0/all/0/1">Soledad Villar</a> (JHU) There are many uses for linear fitting; the context here is interpolation and denoising of data, as when you have calibration data and you want to fit a smooth, flexible function to those data. Or you want to fit a flexible function to de-trend a time series or normalize a spectrum. In these contexts, investigators often choose a polynomial basis, or a Fourier basis, or wavelets, or something equally general. They also choose an order, or number of basis functions to fit, andRead More →