Likelihood-based inference

Learning objectives

• Learn the terminology associated with likelihood-based inference
• Derive closed-form expressions for the maximum likelihood estimator in simple models
• Using numerical optimization, obtain parameter estimates and their standards errors using maximum likelihood
• Use large-sample properties of the likelihood to derive confidence intervals and tests
• Use information criteria for model selection

Content

• Course notes: chapter 3 (Likelihood-based inference)

In class

• Slides (pdf and code)

Slides

 View slides in new window  Download PDF of slides

After class

• Exercise sheet 3

S	Content	PDF	Videos	R
3	Likelihood-based inference

Additional readings

• Understanding Maximum Likelihood by Kristoffer Magnusson
• Davison (2003), Chapter 4

References

Davison, A. C. (2003). Statistical models. Cambridge University Press.