# Set S of coordinates (x,y), and am estimating f(x)=ax+b where a>0. ∀x,y((x,y)∈S⟹y<f(x)).

set $S$ of coordinates $\left(x,y\right)$, and am estimating $f\left(x\right)=ax+b$ where $a>0$. I also happen to know that $\mathrm{\forall }x,y\left(\left(x,y\right)\in S\phantom{\rule{thickmathspace}{0ex}}⟹\phantom{\rule{thickmathspace}{0ex}}y.
The question is how I can utilize this knowledge of the upper bound on values to improve the regression result?
My intuition is to run a "normal" linear regression on all coordinates in $S$ giving $g\left(x\right)$ and then construct ${g}^{\prime }\left(x\right)=g\left(x\right)+c$, with $c$ being the lowest number such that $\mathrm{\forall }x,y\left(\left(x,y\right)\in S\phantom{\rule{thickmathspace}{0ex}}⟹\phantom{\rule{thickmathspace}{0ex}}y\le {g}^{\prime }\left(x\right)\right)$, e.g. such that ${g}^{\prime }\left(x\right)$ lies as high as it can whilst still touching at least point in $S$. I do, however, have absolutely no idea if this is the best way to do it, nor how to devise an algorithm that does this efficiently.
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Kody Larsen
Your suggestion to do an ordinary regression and then move it up is a fine way to go about it. This is pretty easy, especially if you have a decent statistics library:
1. Fit the regression.
2. Calculate the residuals, the difference between the y coordinate of each point and the y coordinate of the line at that point, given by ${\stackrel{^}{\beta }}_{0}+{\stackrel{^}{\beta }}_{1}{x}_{i}$, where ${x}_{i}$ is the ${x}_{}$ coordinate of the point. There may be a built-in way to do this. There are matrix-algebra representations as well.
3. Find the largest residual. Not largest in absolute value, just straight-up largest.
4. Add the value of the largest residual to the intercept ${\stackrel{^}{\beta }}_{0}$ of your regression model.
Your regression line now passes through the highest point and is above all the other points.