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Provide intermediate steps for mathematical derivation in scientific paper

From time to time I could use someone to help me understand mathematical derivations in publish

articles which are skipping over to many details for me. :-)

Your first job would be to explain how to use the Taylor expansion to form a

quadratic approximation of the logistic regression log-likelihood in order to

derive a iterative re-weighted least squares algorithm.

More precisely I need the intermediate steps from from eq. 14 to

eq. 15. [0].

The reason I need the intermediate steps from eq. 14 to eq. 15 is that I

want to apply the same steps instead to a linear model $f(x) = x^T\theta$ (as in the

paper) to a different model.

Here are some references that might be helpful.

- [login to view URL]~mgormley/courses/10701-f16/slides/[login to view URL] slide 60

- [login to view URL] slide 8

- [login to view URL]

- [login to view URL]:doi/10.1371/[login to view URL]

- [login to view URL]

section 2

- [login to view URL]~ryantibs/convexopt-F15/lectures/[login to view URL]

[0] Regularization Paths for Generalized Linear Models via Coordinate Descent

[login to view URL]

Skills: Algorithm, Mathematics, Statistics

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