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 (S3005/2024Fall/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. 

In-class notes

* Click (S3005/2024Fall/inclassNote) and (S3005/2024Fall/recording) to download in-class notes and recordings (if any).
* In-class notes and recordings (if any) will be uploaded within one week after the lecture.

• • Lecture 1 (4 Sep) --- Review of statistics, theory of rank and signs 

  • Lecture 2 (11 Sep) --- Properties of rank, rank/sign-type tests 

  • Lecture 3 (25 Sep) --- Theory of rank-type tests, rank-sum test

  • Lecture 4 (2 Oct) --- Methods of rank-type tests (sign test, signed-rank test, rank-sum test, trend test, Ansari–Bradley test), implementation 

  • Lecture 5 (9 Oct) --- Strong/weak correlation, Pearson's correlation, Spearman's correlation, Kendall's correlation, Bergsma-Dassios's correlation 

  • Lecture 6 (16 Oct) --- Bergsma-Dassios's correlation, Chatterjee's correlation, power curve 

  • Lecture 7 (23 Oct) --- Coding exercise related to correlation, distribution test

  • Lecture 8 (30 Oct) --- KS, CvM, AD distribution test, 2-sample test, Lilliefors normality test

  • Lecture 9 (6 Nov) --- permutation test, coding exercise, causal inference

  • Lecture 10 (13 Nov) --- Jackknife, bootstrap variance estimator, bootstrap confidence interval 

  • Lecture 11 (20 Nov) --- percentile CI, studentized pivotal CI, bias-corrected & accelerated CI, proof for bootstrap CI, histogram and its theory 

  • Lecture 12 (27 Nov) --- KDE, optimal bandwidth, rule of thumb bandwidth 

  • Lecture 13 (2 Dec) --- cross-validation & plug-in bandwidth, nonparametric regression, N-W kernel estimator, local polynomial estimator 

Mid-term project

• Start time: 25 October (Friday) @ 7:00 pm 

• End time: 27 October (Sunday) @ 7:00 pm 

• Duration: 48 hours

• Scope: Chapters 1--4 

• Instructions: The detailed instructions are stated on the first page of the question paper. Some highlights are listed below:

  • • Read the instructions carefully before doing the exam.

    • Complete the project by yourself without discussion and consultation.

    • Use any official course materials if you wish.

    • The project will be graded according to the criterion referencing scheme (see the last page of mock solution).

• Submission

  • • Compile your answers in a single ".pdf" file (i.e., not MS words, jpeg, zip, etc). 

    • Sign the Honor Code, and attach it as a cover of your submitted file. 

    • Name the document in the format S4010_M_sid_name.pdf, e.g., S4010_M_1155001234_ChanKinWai.pdf. 

    • Submit to Blackboard. You may submit your answers as many times as you wish, however, only the last submission will be graded. 

    • All plots, numerical answers, simulation results, etc must be included in the written part. Graders will not run your submitted codes to check the answers.

    • 
      

• Mock mid-term projects
  MOCK mid-term project (with suggested solutions & students' answers. Note that they may not be error-free.)   

  • • Mock 1 (2020 Fall M0)

    • Mock 2 (2020 Fall M)

    • Mock 3 (2021 Fall M0)

    • Mock 4 (2021 Fall M)

    • Mock 5 (2022 Fall M) 

    • Mock 6 (2023 Fall M) *** Most relevant ***

• REAL mid-term project

  • • 2024Fall_S3005_M (password will be sent to your CUHK email sharply at the project start time)