Local regression

Formal sciences
Statistics
Statistical inference
Non-parametric statistics

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Related lectures (31)

Modern Regression: Overdispersion and Model Assessment

Explores overdispersion, model assessment, and regression techniques for count data.

Generalized Additive Models: Penalized Iterative Weighted Least Squares

Covers the introduction to generalized additive models and iterative weighted least squares for model checking and smooth fits.

Kernel Methods: SVM and Regression

Introduces kernel methods like SVM and regression, covering concepts such as margin, support vector machine, curse of dimensionality, and Gaussian process regression.

Linear & Weighted Regression

Covers linear and weighted regression, limitations, and introduces locally weighted regression for model-free regression.

Gaussian Process Regression: Kernels and Comparisons

Explores Gaussian Process Regression kernels, computational costs, and comparisons with Ridge Regression and other non-linear regression techniques.

Neural Networks: Basics and Applications

Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.

Error Decomposition and Regression Methods

Covers error decomposition, polynomial regression, and K Nearest-Neighbors for flexible modeling and non-linear predictions.

Gaussian Mixture Regression: Modeling and Prediction

Covers Gaussian Mixture Regression principles, modeling joint and conditional densities for multimodal datasets.

Generalized Linear Models

Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.

Hypothesis Testing and Non-Parametric Regression

Covers worked examples on hypothesis testing and non-parametric regression.

Bivariate Data Analysis: Correlation and Regression

Explores bivariate data analysis, correlation, and regression techniques for model assessment.

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