I don’t have a good answer to that. The curve was generated using LOESS, which I haven’t studied, and I assume that the shaded area has a interpretation in that framework.
I suspect it’s parameter uncertainty rather than data uncertainty—that is, instead of showing the the fit plus/minus one stdev so you can check that about two-thirds of the data points fall in that rectangle, it’s giving you a sense of what family of fit lines all fit the data ‘well enough’ (i.e. within some distance of the best fit).
That’s probably it. When fitting a line using MCMC you’ll get an anticorrelated blob of probabilities for slope and intercept, and if you plot one deviation in the fit parameters you get something that looks like this. I’d guess this is a non-parametric analogue of that. Notice how both grow significantly at the edges of the plots.
What’s the shaded area in the very first plot? Usually this area is one deviation around the fit line, but here it’s clearly way too small to be that.
I don’t have a good answer to that. The curve was generated using LOESS, which I haven’t studied, and I assume that the shaded area has a interpretation in that framework.
I suspect it’s parameter uncertainty rather than data uncertainty—that is, instead of showing the the fit plus/minus one stdev so you can check that about two-thirds of the data points fall in that rectangle, it’s giving you a sense of what family of fit lines all fit the data ‘well enough’ (i.e. within some distance of the best fit).
That’s probably it. When fitting a line using MCMC you’ll get an anticorrelated blob of probabilities for slope and intercept, and if you plot one deviation in the fit parameters you get something that looks like this. I’d guess this is a non-parametric analogue of that. Notice how both grow significantly at the edges of the plots.