This course aims to introduce the nonparametric regression techniques, essentially referring to smoothing procedures for curve estimation, that provide a flexible approach to explore the relationship between a response and a few associated covariates without specifying a parametric model. Those commonly employed techniques (such as kernel smoothing methods and basis-based approaches) along with their statistical properties will be introduced. Some related topics such as dimension reduction and functional data analysis will be covered as well.

Those commonly employed approaches for nonparametric regression will be introduced. After taking the course, students are expected to comprehend the fundamental, utilize the approaches properly and perform sensible data analysis in addition to be familiar with research questions in this domain.

Calculus, Statistics, and Linear Regression.

1. Hastie, Tibshirani and Friedman (2016). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd edition. Springer. https://hastie.su.domains/ElemStatLearn/ 2. Scott (2015). Multivariate Density Estimation: Theory, Practice, and Visualization. 2nd Edition. Wiley. 3. Takezawa (2005). Introduction to Nonparametric Regression. Wiley 4. Gyorfi, Kohler, Krzy?ak and Walk (2002). A Distribution-Free Theory of Nonparametric Regression. Springer. 5. Tsybakov (2009). Introduction to Nonparametric Estimation. Springer. 6. Wahba (1990) Spline Models for Observational Data (https://epubs.siam.org/doi/book/10.1137/1.9781611970128)