Computational Statistics

eth zurich spring 2025

In “Computational Statistics” we discuss modern statistical methods for data analysis, including methods for data exploration, prediction and inference. We pay attention to algorithmic aspects, theoretical properties and practical considerations and obtain an overview of modern statistical methods for data analysis, including their algorithmic aspects and theoretical properties.

Contents

[click titles to navigate]
  1. Multiple Linear Regression
    1. The Linear Model
    2. Ordinary Least Squares
    3. Tests and Confidence Intervals
    4. Check of Model Assumptions
    5. Generalizing the Linear Model
  2. Nonparametric Density Estimation
    1. Estimation of a Density
    2. The Role of the Bandwidth
  3. Nonparametric Regression
    1. The Kernel Regression Estimator
    2. The Local Polynomial Estimator
    3. The Smoothing Splines Estimator
  4. Cross Validation
    1. Training and Test Set
  5. Bootstrap