Zhou Fan, Stanford University, Autumn 2016
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!)
Approximately weekly, due Wednesdays at 5PM. Late homeworks will NOT be accepted (unless with advance written permission from the teaching staff). Your lowest homework grade will be dropped.
Homework assignments will include simple computing exercises asking you to perform small simulations, create histograms and plots, and analyze data. You may use any language (e.g. R, Python, Matlab) and will be graded only on your results, not on the quality of your code.
You are encouraged to discuss homework problems with your classmates, but you must submit your own individual homework write-up, in your own words and using your own code for the programming exercises. Please indicate at the top of your write-up the names of the students with whom you worked.
John A. Rice, Mathematical Statistics and Data Analysis, 3rd edition.
Morris H. DeGroot and Mark J. Schervish, Probability and Statistics, 4th edition.
Larry Wasserman, All of Statistics: A concise course in statistical inference.