>> L = \sum_{i=1}^b \sum_{j=1}^k (y_ij - \beta_i - \mu_j)^2
>
> To narrow it down, could you say what you think the derivative of the
> above loss function is with respect to beta_i?
The partial derivatives i get are:
dL/d(beta_i) = -2 \sum_{i=1}^b \sum_{j=1}^k (y_ij - \beta_i - \mu_j)
dL/d(mu_j) = -2 \sum_{i=1}^b \sum_{j=1}^k (y_ij - \beta_i - \mu_j)
for all i's and j's respectively.
> (This is homework, right?)
No, it's actually a question in a book, "Mathematical Statistics And It's
Applications" by Larsen and Marx. It's question 13.2.12 on page 682 if you
happen to have it in your bookshelf.
--
Kindly
Konrad
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