Week 1

• Course introduction
• Lecture: Review of likelihood inference and asymptotic results

Week 2

• Lecture: The likelihood family: Profile, marginal, conditional, quasi, empirical, etc.
• Discussion
  • Background: Likelihood Inference by Nancy Reid
  • Application: PIE: A prior knowledge guided integrated likelihood estimation method for bias reduction in association studies using electronic health records data by Jing Huang et al.

Week 3

• Lecture: Likelihood inference under model misspecification
• Discussion
  • Article: Automated feature selection of predictors in electronic medical records data by Jessica Gronsbell et al.

Week 4

• Lecture: Penalized likelihood
• Discussion on High-dimensional likelihood inference
  • Article: A modern maximum-likelihood theory for high-dimensional logistic regression by Pragya Sur and Emmanuel J. Candès

Week 5

• Final Presentations

Week 6

• Final Presentations