Stanford Stat 200: Introduction to Statistical Inference

Lecturer: Art B. Owen

Introduction to Statistical Inference.zip
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Description

• Statistical concepts and methods developed in a mathematical framework: Hypothesis testing, point estimation, confidence intervals. Neyman-Pearson theory, maximum likelihood estimation, likelihood ratio tests, Bayesian analysis. Asymptotic theory and simulation-based methods.
  

Prerequisites: Probability theory (STATS 116), multivariable calculus (MATH 52), and basic computer programming (or willingness to learn as you go!)

• Textbook
  

John A. Rice, Mathematical Statistics and Data Analysis, 3rd edition.

For reference:

Morris H. DeGroot and Mark J. Schervish, Probability and Statistics, 4th edition.

Larry Wasserman, All of Statistics: A concise course in statistical inference.