Preliminaries
Order statistics
Stirling's formula
Convex and concave functions
Convexity based inequalities: Jensen, Holder, Cauchy
Schwartz
Order based inequality: association inequality for
increasing and decreasing functions
Elementary inequalities for probabilities: Markov,
Chebyshev, and Chernoff
Moment generating functions
Statistical Inference
Decision theoretic
framework, including loss, risk and admissibility
Location and scale families
Exponential families, including canonical exponential
families
Sufficiency and the factorization theorem
Minimal sufficiency
Ancillary and complete statistics, Basu's theorem
Likelihood
Point Estimation
Method of moments
Maximum likelihood estimation, including evaluation of
maxima
Bayesian point estimators, including prior and posterior
distributions, and conjugate families
Unbiased estimators
Squared error loss and the bias-variance decomposition
Best unbiased estimators
The Cramer-Rao inequality and Fisher Information
Rao-Blackwell theorem
Bayes risk and Bayes rules
Hypothesis Testing
Likelihood ratio tests
Power function and Type I, II errors
Level, size and unbiasedness
Uniformly most powerful tests: Neyman-Pearson Lemma,
tests based on sufficient statistics
Monotone likelihood ratio and Karlin-Rubin Theorem
p-values
Interval Estimation
Coverage probability and
confidence coefficient
Inverting hypothesis tests
Confidence sets from pivots
Pivoting CDFs