In this picture you can see the measured datapoints (blue) and a curve I fit in (orange). The value with x=10000 seems like an outlier, and I am thinking about removing it, to get a better fitting curve. However, I don't want to remove it manually.
Methods I considered:
- Trim at y<0.55. seems crude and unreliable, since the data can change.
- remove points with a big vertical distance to the neighboring points. However, the vertical distance isn't that big, compared to the datapoints with bigger x.
- IQR; only few datapoints, and the point isn't that far out.
- fit curve iteratively and with each iteration remove one of the points. Subsequently observe how well the curve fits. With this Method I fear losing points which actually contribute valid information. Furthermore there could be more than one outlier.
Could anyone point me in the right direction? Any help is appreciated!