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A ssignment one presentation

A ssignment one presentation. Question 2.5 (a)-(d). by Hao Ding. Derive Newton-Raphson Update. A Starting point First derivative of Log – Likelihood Second derivative of Log – Likelihood. Newton-Raphson: Derivation. Newton-Raphson: Derivation. Newton-Raphson: Implementation.

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A ssignment one presentation

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  1. Assignment one presentation Question 2.5 (a)-(d) byHao Ding

  2. Derive Newton-Raphson Update A Starting point First derivative of Log – Likelihood Second derivative of Log – Likelihood

  3. Newton-Raphson: Derivation

  4. Newton-Raphson: Derivation

  5. Newton-Raphson: Implementation solve(gpp) %*% gp

  6. Fisher Scoring Compute the Fisher Information Using Fisher Information to replace second derivative

  7. Newton VS Fisher Scoring Implementation cost • Fisher Scoring is easier to implement: less computational cost Performance

  8. Estimate Standard Error of MLEs . Evaluate the Fisher Information with MLEs The standard errors are the diagonal terms of the inverse of Fisher Information

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