STATS 205 (Spring 2019, Stanford)

Description

Course link.

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
  • Time: Mon, Wed, Fri 1:30 PM - 2:20 PM
  • Location:
  • LEC: 04/01/2019 - 06/05/2019 (10 Weeks - 30 hours)

Prerequisites

  • STATS 60 or STATS 110 or STATS 160; Students should familiar with summary statistics, hypothesis testing, point estimation, interval estimation (confidence intervals), basics of statistical inference, and R.

Texts

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

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()

Grading

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 gives an overview of some current research topics; install R; install RStudio; TryR, R Markdown webinar, R Markdown provide adequate initiation to R and R Markdown.
04/03/2019 Week 1 Lecture 1 Logistics and Preliminaries Syllabus, HWC: Chapter 1
04/05/2019 Week 1 Lecture 2 The One-sample problem I (testing procedure) HWC: Chapter 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: Chapter 3.4-3.6, 3.8, 3.1-3.3, 3.7
04/10/2019 Week 2 Lecture 4 Statistical functionals and Influence functions Notes will be posted on W: Chapter 2, ET: Chapter 4, 5, 21.3
04/12/2019 Week 2 Lecture 5 Jackknife and Bootstrap I HWC: Chapter 8.4 and notes will be posted based on W: Chapter 3, DH, ET: Chapter 6, 11 Homework 2 Posted, Homework 1 Due
04/15/2019 Week 3 Lecture 6 Bootstrap II Notes will be posted based on ET: Chapter 23, Re:DH1994, Re:DH1995
04/17/2019 Week 3 Lecture 7 Discrete data problems I HWC: Chapter 2
04/19/2019 Week 3 Lecture 8 Discrete data problems II HWC: Chapter 10 Homework 3 Posted, Homework 2 Due
04/22/2019 Week 4 Lecture 9 Two-sample problem I HWC: Chapter 4
04/24/2019 Week 4 Lecture 10 Two-sample problem II HWC: Chapter 5
04/26/2019 Week 4 Lecture 11 Permutation Test I Homework 4 Posted, Homework 3 Due
04/29/2019 Week 5 Lecture 12 Permutation Test II
05/01/2019 Week 5 Lecture 13 Ranked-based linear regression HWC: Chapter 9
05/03/2019 Week 5 Lecture 14 Smoothing I W: Chapter 4 Midterm project proposal due
05/06/2019 Week 6 Lecture 15 Nonparametric regression I HWC: Chapter 9.7, 14, W: Chapter 5
05/08/2019 Week 6 Lecture 16 Nonparametric regression II HWC: Chapter 9.7, 14, W: Chapter 5
05/10/2019 Week 6 Lecture 17 Wavelets HWC: Chapter 13, W: Chapter 9 Homework 5 Posted, Homework 4 Due
05/13/2019 Week 7 Lecture 18 ANOVA I HWC: Chapter 6, 7
05/15/2019 Week 7 Lecture 19 ANOVA II , multiple comparison HWC: Chapter 6, 7
05/17/2019 Week 7 Survival analysis I HWC: Chapter 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: Chapter 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: Chapter 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: Chapter 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 are available here.

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