- Review of likelihood function of parameters
- Fundamental Concepts
- Covariance and Covariance matrix
- Covariance of MLE
- Score/gradient function
- Observed Hessian and Fisher information matrices
- Covariance matrix of MLE
- Asymptotic Sampling Distribution of MLE
- Multivariate normal distribution
- Asymptotic sampling distribution of MLE
- Implementing with R Functions
- using R wrapper functions for optimization with caution
- Notes and code
- Asymptotic Sampling Distribution of the MLE
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- A Slightly Technical Note on the MLE
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- Section 1: examples 1, 2, and 11 (optional)
- Section 4: univariate asymptotic properties
- Section 5: multivariate asymptotic properties
- A Big-picture View of R Tools for Optimization:
jstatsoft paper
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- Suggested R Functions for Optimization
- General:
optim(), nlm(), nlminb()
- Specialized:
optimx()
- Global:
DEoptim()
- Midterm Exam
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