Some Basic Theory for Statistical Inference

Probability Distribution function Lebesgue measure Probability measure Likelihood principle Measure Dispersion matrix Likelihood ratio Asymptotic Power Asymptotic Relative Efficiency conditional distributions Continuous Distribution Function Discrete Sample Space Continuous Probability Measure Glivenko Cantelli Theorem Discrete Probability Space Discrete Probability Measure Negative Binomial Families Posterior Distributions Independent Uniform Random Variables Sample Distribution Function Real Valued Statistic Left Hand Derivative Neyman Pearson Theorem Sample Path Sufficient Regularity Conditions