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 a mid-term exam (both written part and coding part); and

    • f (out of 100) is the score of a final exam (both written part and coding part);

    • 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, 0.2a + 0.3max(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 up-to-date information, please always refer to the course outline announced by the course instructor in Blackboard, which shall prevail over the above information if there is any discrepancy.

Lecture Notes

* Click (S3005/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: xxx (xxx) @ xxx pm  (Please arrive 15 minutes earlier.)

• Duration: TBA

• Location: TBA

• Scope: TBA

• Instructions: See the Blackboard announcement.

• Seat plan: TBA

• Mock papers: