Partial least squares regression

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Statistical inference
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Related lectures (28)

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PCA: Directions of Largest Variance

Covers PCA, finding directions of largest variance, data dimensionality reduction, and limitations of PCA.

Principal Component Analysis: Applications and Limitations

Explores the applications and limitations of Principal Component Analysis, including denoising, compression, and regression.

Principal Component Analysis: Dimensionality Reduction

Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.

Modeling Elasticity and Coefficients

Explores correlation matrices, regression, variance, confidence intervals, and standardized systems in statistical modeling.

Clustering Methods

Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.

Regression: Linear Models

Explores linear regression, least squares, residuals, and confidence intervals in regression models.

Distributed Algorithms for Networked Control Systems

Explores distributed algorithms for networked control systems, covering consensus, least-squares regression, and time-varying communication networks.

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.

Factor Models: Latent Variables and Asset Pricing

Covers Factor Models, including PCA, asset pricing, factor investing, GMM, and Fama-McBeth estimation.

Applying the learning bound to kernel regression

Discusses the application of the main theorem to least square regression in a RKHS, focusing on LR of the Rademacher bound and Lipschitz constant.