STATS 205 (Spring 2019, Stanford)
Description
Syllabus
Course Overview
This course covers nonparametric analogs of the one- and two-sample t-tests and analysis of variance; the sign test, median test, Wilcoxon’s tests, and the Kruskal-Wallis and Friedman tests, tests of independence; nonparametric regression and nonparametric density estimation; modern nonparametric techniques; nonparametric confidence interval estimates.
Expected outcomes
By the end of the course, the student should be able to
- understand the assumptions underlying the nonparametric methods
- apply nonparametric methods to modern data analysis problems
- get hands-on experience in implementing methods and using existing R packages.
Course Information
- Term: Spring 2019
- Units: 3
Prerequisites
Texts
- Required:
- (HWC) Nonparametric Statistical Methods.
- Authors: Myles Hollander, Douglas A. Wolfe, Eric Chicken
- Edition: \(3^{rd}\) Edition
- Print ISBN:9780470387375
- Online ISBN:9781119196037
- (HWC) Nonparametric Statistical Methods.
- Recommended:
- (DH): Davison and Hinkley (1997). Boostrap Method and Their Application.
- (ET): Efron and Tibshirani (1994). An Introduction to the Bootstrap.
- (KM): Kloke and McKean (2015). Nonparametric Statistical Methods Using R.
- (L): Lehmann (2006). Nonparametrics: Statistical Methods Based on Ranks.
- (RHG): Ramsay, Hooker, Graves (2009). Functional Data Analysis with R and MATLAB.
- (W): Wasserman (2006). All of Nonparametric Statistics.
Other sources
- Recommended Readings: a reading list will be posted on Canvas.
- Re:BHLLSW2009: Buja, Cook, Hofmann, Lawrence, Lee, Swayne, and Wickham (2009). Statistical Inference for Exploratory Data Analysis and Model Diagnostics.
- Re:D1983: Diaconis (1983). Theories of Data Analysis: From Magical Thinking Through Classical Statistics.
- Re:DH1994: Diaconis and Holmes (1994). Gray Codes for Randomization Procedures.
- Re:DH1995: Diaconis and Holmes (1995). Discrete Probability and Algorithms: Three Examples of Monte-Carlo Markov Chains: At the Interface Between Statistical Computing, Computer Science, and Statistical Mechanics, pg. 43-56.
- Re:JWH2014: Josse, Wager, and Husson (2014). Confidence Areas for Fixed-Effects PCA.
- Useful links
- Li:H1997: Holmes (1997). Lecture Notes on Computer Intensive Methods in Statistics.
- Li:H2004: Holmes (2004). Lecture Notes on Complete Enumeration.
- Li:C2016: Seiler (2016). Lecture Notes on Nonparametric Statistics.
- Li:W2016: Wasserman (2016). Lecture Notes on Nonparametric Bayesian Methods.
Software
- In this course, we will use R for computing and R Markdown for producing lecture slides, solutions for homework assignments. R Markdown/Latex is highly recommended to write the midterm project proposal report and final project report. Install the following software:
- R (required): https://www.r-project.org/.
- R Studio is highly recommended for syntax highlighting, package management, document generation, and more: https://www.rstudio.com/.
- The newest version of R Studio is highly recommended (v1.1.463).
- Latex, which will enable you to create PDFs directly from the R Markdown in RStudio.
- Latex, which will enable you to create PDFs directly from the R Markdown in RStudio.
- Install Tinytex
- install.packages(‘tinytex’)
- tinytex::install_tinytex()
Evaluation
The final letter grade for this course will be determined by each method of assessment weighted as follows:
- Class participation (5%)
- Weekly homework assignments (50%)
- Midterm project proposal (10%, due on 05/03/2019)
- Final project (35%, due on 06/05/2019)
Lecture Notes
Course Schedule
Date | Week | Topic | Reading | Notes |
---|---|---|---|---|
04/01/2019 | Week 1 Lecture 0 | Overview of current research in nonparametric and adequate initiation to R and R Markdown | ASA Nonparametric statistics section news, install R, install RStudio; TryR, R Markdown webinar, R Markdown | |
04/03/2019 | Week 1 Lecture 1 | Logistics and Preliminaries | HWC: 1 | |
04/05/2019 | Week 1 Lecture 2 | The One-sample problem I (testing procedure) | HWC: 3.4-3.6, 3.8, 3.1-3.3, 3.7 | Homework 1 posted |
04/08/2019 | Week 2 Lecture 3 | The One-sample problem II (estimator associated with the statistic, confidence interval, example) | HWC: 3.4-3.6, 3.8, 3.1-3.3, 3.7 | |
04/10/2019 | Week 2 Lecture 4 | Statistical functionals and Influence functions | W: 2, ET: 4, 5, 21.3 | |
04/12/2019 | Week 2 Lecture 5 | Jackknife and Bootstrap I | HWC: 8.4, W: 3, DH, ET: 6, 11 | Homework 2 Posted, Homework 1 Due |
04/15/2019 | Week 3 Lecture 6 | Bootstrap II | ET: 23, Re:DH1994, Re:DH1995 | |
04/17/2019 | Week 3 Lecture 7 | Discrete data problems I | HWC: 2 | |
04/19/2019 | Week 3 Lecture 8 | Discrete data problems II | HWC: 10 | Homework 3 Posted, Homework 2 Due |
04/22/2019 | Week 4 Lecture 9 | Two-sample problem I | HWC: 4 | |
04/24/2019 | Week 4 Lecture 10 | Two-sample problem II | HWC: 5 | |
04/26/2019 | Week 4 Lecture 11 | Permutation Test I | ET: 15, Li:H1997 | Homework 4 Posted, Homework 3 Due |
04/29/2019 | Week 5 Lecture 12 | Permutation Test II | Li:H1997, Li:C2016 | |
05/01/2019 | Week 5 Lecture 13 | Ranked-based linear regression | HWC: 9 | |
05/03/2019 | Week 5 Lecture 14 | Smoothing I | W: 4 | Midterm project proposal due |
05/06/2019 | Week 6 Lecture 15 | Nonparametric regression I | HWC: 9.7, 14, W: Chapter 5 | |
05/08/2019 | Week 6 Lecture 16 | Nonparametric regression II | HWC: 9.7, 14, W: Chapter 5 | |
05/10/2019 | Week 6 Lecture 17 | Wavelets | HWC: 13, W: Chapter 9 | Homework 5 Posted, Homework 4 Due |
05/13/2019 | Week 7 Lecture 18 | ANOVA I | HWC: 6, 7 | |
05/15/2019 | Week 7 Lecture 19 | ANOVA II , multiple comparison | HWC: 6, 7 | |
05/17/2019 | Week 7 | Survival analysis I | HWC: 10 | Homework 6 Posted, Homework 5 Due |
05/20/2019 | Week 8 | Survival analysis II | HWC: Chapter 10 | |
05/22/2019 | Week 8 | Ranked set sampling | HWC: 15 | |
05/24/2019 | Week 8 | Bayesian nonparametric I | HWC: Chapter 16, Li:W2016 | Homework 7 Posted, Homework 6 Due |
05/27/2019 | Week 9 | (Holiday, no classes) | Memorial Day | |
05/29/2019 | Week 9 | Bayesian nonparametric II | HWC: 16, Li:W2016 | |
05/31/2019 | Week 9 Lecture 25 | Inference for data visualization | Re:BHLLSW2009, Re:D1983, Re:JWH2014 | Homework 7 Due (no late submission allowed, End-Quarter Period starts) |
06/03/2019 | Week 10 Lecture 26 | Bootstrap III | ET: 12, 14 | |
06/05/2019 | Week 10 Lecture 27 | Wrap-up | Final project due |
R Markdown files
R Markdown files to create the lecture slides and PDFs are available in https://github.com/PratheepaJ/STATS205.