Intermediate Probability and Statistical Theory II
Fundamental theory and methods for statistical inference. Topics include data reduction (sufficient, ancillary, and complete statistics), estimation (method of moments, maximum likelihood estimators, Bayes estimators), evaluating methods (mean squared error, best unbiased estimators, asymptotic evaluations), hypothesis testing, and confidence intervals.
Topics
- data reduction (sufficient
- ancillary
- and complete statistics)
- estimation (method of moments
- maximum likelihood estimators
- Bayes estimators)
- evaluating methods (mean squared error
- best unbiased estimators
- asymptotic evaluations)
- hypothesis testing
- and confidence intervals
Topics explicitly listed in official catalogue description
Prerequisites & course information
Fundamental theory and methods for statistical inference. Topics include data reduction (sufficient, ancillary, and complete statistics), estimation (method of moments, maximum likelihood estimators, Bayes estimators), evaluating methods (mean squared error, best unbiased estimators, asymptotic evaluations), hypothesis testing, and confidence intervals.
Sources
- Course catalogue ↗2026-2027