Consider the model $$y_i = Bx_i + \epsilon_i$$
where $\epsilon_1, \ldots , \epsilon_n$ are independent and identically distributed as normal with mean $0$ and variance $\sigma^2$.
I know that if the model was $y_i = A + Bx_i + \epsilon_i$ I would have $$\sum_{i=1}^n (\epsilon_i)^2 = \sum_{i=1}^n(y_i - A - Bx_i)^2 $$
And then I would find the partial derivatives of $A$ and $B$ and set them equal to $0$ so I can find their estimators.
How is this done when there is no $A$??
Similar method would apply.
You would minimize $$\sum_{i=1}^n ( y_i - Bx_i)^2$$
Find the partial derivative of the function with respect to $B$ and then set them to $0$ so that you can find the estimator.