Part XI — Parameter Estimation¶
Turning data into parameter estimates: point estimators and their properties, the two classical recipes (maximum likelihood and method of moments), their Bayesian counterparts, and interval estimates.
Topics¶
| Topic | Focus |
|---|---|
| Point Estimation | Statistics, estimators, and quadratic risk |
| Properties of Estimators | Bias, variance, consistency, and efficiency |
| Maximum Likelihood Estimation | Estimating parameters by maximizing the likelihood |
| Method of Moments | Matching sample moments to model moments |
| Bayesian Estimation | Estimation from the posterior distribution |
| Maximum A Posteriori Estimation | The posterior mode as a point estimate |
| Confidence Intervals | Interval estimates and what their coverage actually means |
| Bootstrap Confidence Intervals | Confidence intervals from resampled statistics |