STAT 3005 (CUHK)

Description

This course introduces a wide variety of nonparametric techniques for performing statistical inference and prediction, emphasizing both conceptual foundations and practical implementation. Basic theoretical justification is also provided. The content covers three broad themes: (i) rank-type and order-type methods for handling location, dispersion, correlation, distribution and regression problems, (ii) resampling-type procedures for testing and assessing precision, and (iii) smoothing-type techniques for estimation and prediction. Topics include Wilcoxon signed-rank test, Mann-Whitney rank sum test, Spearman’s rho, Kendall’s tau, Kruskal-Wallis test, Kolmogorov-Smirnov test, bootstrapping, Jackknife, subsampling, permutation tests, kernel method, k-nearest neighbour, tree-based method, classification, etc. 

Note: No prerequisite but knowledge of Stat 2001, 2005 and 2006 is strongly recommended.

Learning outcomes

Upon finishing the course, students are expected to 

• • 1. appreciate the beauty of nonparametric methods; 

    2. apply a wide variety of nonparametric techniques to perform inference, prediction and learning tasks; 

    3. understand the pros and cons of parametric and nonparametric methods; 

    4. master the skills in deriving basic theoretical properties of nonparametric methods; 

    5. use computer programs to perform nonparametric statistical analysis for real-life problems. 

Academic years

• 2023-24 Fall. (CTE-score: 6.00/6)

• 2022-23 Fall. (CTE-score: 5.92/6)

• 2021-22 Fall. (CTE-score: 5.96/6)

• 2020-21 Fall. (CTE-score: 5.89/6)