Browse: Keywords: Local Weighted Regression

Essentials of Probability and Statistical Inference IV: Algorithmic and Nonparametric Approaches

• Abstract: Introduces the theory and application of modern, computationally-based methods for exploring and drawing inferences from data. Covers re-sampling methods, non-parametric regression, prediction, and dimension reduction and clustering. Specific topics include Monte Carlo simulation, bootstrap cross-validation, splines, local weighted regression, CART, random forests, neural networks, support vector machines, and hierarchical clustering. De-emphasizes proofs and replaces them with extended discussion of interpretation of results and simulation and data analysis for illustration.

• Subject: Science and Technology, Social Sciences, Mathematics and Statistics
• Grade Level: Post-secondary
• Collection: JHSPH OpenCourseWare