STAT 3005

Textbooks 

A self-contained lecture note is the main source of reference. Complementary textbooks include 

• • • (Major) Bonnini, S., Corain, L., Marozzi, M., and Salmaso, L. (2014). Nonparametric hypothesis testing: rank and permutation methods with applications in R. Wiley.  

    • (Major) Wasserman, L. (2006). All of nonparametric statistics. Springer. 

    • (Minor) Wasserman, L. (2004). All of Statistics: A Concise Course in Statistical Inference. Springer.  

    • (Minor) James, G., Witten, D., Hastie, T., and Tibshirani, R (2013). An Introduction to Statistical Learning: with Applications in R. Springer.  

Assessment and Grading 

There are three main assessment components, plus a bonus component. 

• • •  a (out of 100) is the average score of approximately eight assignments with the lowest two scores dropped; 

    • m (out of 100) is the score of mid-term project; and

    • f (out of 100) is the score of final project.

    • b (out of 2) is the bonus points, which will be given to students who actively participate in class. 

The total score t (out of 100) is given by 

t = min{100, 0.3a + 0.2max(m,f) + 0.5f + b}

If min(t, f ) < 30, the final letter grade will be handled on a case-by-case basis. Otherwise, your letter grade will be in the A range if t ≥ 85, at least in the B range if t ≥ 65, at least in the C range if t ≥ 55.

* For the most updated information, please always refers to the course outline announced by the course instructor in Blackboard, which shall prevail the above information if there is any discrepancy.

Lecture Notes

* Click (S3005/2025Fall/lecture) to download lecture notes (or click the individual links below).
* The finalized version of the notes will be uploaded one day before the lecture. 
* All rights reserved by the authors. Re-distribution by any means is strictly prohibited.

Front matters

• • • Instructions

Part I: Philosophy and Foundation 

• Chapter 1: Introduction

• Chapter 2: Statistical foundation 

Part II: Rank-type and order-type methods

• Chapter 3: Location and scale problems 

• Chapter 4: Correlation problem 

• Chapter 5: Distribution problem 

Part III: Resampling-type procedures

• Chapter 6: Permutation tests 

• Chapter 7: Bootstrap 

Part IV: Smoothing-type estimation and learning techniques

• Chapter 8: Density estimation

• Chapter 9: Nonparametric regression 

Appendices  

• Appendix A: Basic Mathematics 

• Appendix B: Basic probability 

• Appendix C: Basic Statistics 

• Appendix D: Basic programming in R --- for students who want to review; read Lectures 2 and 3 in RMSC 1101 

• Appendix E: R-codes used throughout the courses can be found in the lecture folder (this folder will be updated from time to time). 

• P.S.: Not all materials in the appendices are directly useful for this course. I will tell you which parts are useful when we need them. 

Mid-term exam

• Start time: 17 October (Friday) @ 6:30 pm 

• Duration: 3 hours

• Location: LSB LT 3 & 5 (tentative)

• Scope: TBA

• Instruction: TBA

• Mock: TBA