Lectures related to Robust regression

Robust Design: Trade-off Management in Mechanical Development

Emphasizes the importance of managing trade-offs for product robustness in mechanical design, using Multi-objective Monotonicity Analysis for quantitative analysis and systematic redesign efforts.

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Regression Methods: Model Building and Inference

Covers Inference, Model Building, Variable Selection, Robustness, Regularised Regression, Mixed Models, and Regression Methods.

Non-parametric Regression: Smoothing Techniques

Explores non-parametric regression techniques, including splines, bias-variance tradeoff, orthogonal functions, wavelets, and modulation estimators.

Linear Regression: Multicollinearity, Outliers, Model Specification

Covers multicollinearity, outliers, model specification, and practical strategies in linear regression.

Genomic Data Analysis: Quality Control Tools

Explores quality control tools in genomic data analysis, emphasizing robust procedures in the presence of outliers and image artifacts.

Instrumental Variables: Addressing Measurement Error and Reverse Causality

Explores how instrumental variables correct biases from measurement error and reverse causality in regression models.

Robust Regression in Genomic Data Analysis

Explores robust regression in genomic data analysis, focusing on downweighting large residuals for improved estimation accuracy and quality assessment metrics like NUSE and RLE.

Regression Methods: Model Building and Diagnostics

Explores regression methods, covering model building, diagnostics, inference, and analysis of variance.

Regression Methods: Model Building and Inference

Covers analysis of variance, model building, variable selection, and function estimation in regression methods.

Linear Regression: Beyond the Basics

Explores advanced concepts in linear regression models, including multicollinearity, hypothesis testing, and handling outliers.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Regression Methods: Spline Smoothing

Covers regression methods focusing on spline smoothing and penalised fitting to balance data fidelity and smoothness.

Data-Driven Modeling: Regression

Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.

Regression Analysis: Model Selection and Diagnostic Tools

Explores regression analysis tools, model selection, influential points, outliers, and diagnostic plots.

Nonlinear Regression: Gaussian Mixture Regression

Explores Gaussian mixture regression, uniqueness of solutions, overfitting, noise handling, and interpolation.

Robust Regression: Methods and Applications

Explores robust and resistant methods in linear models, emphasizing the importance of handling extreme observations and the implications of robustness in regression models.

Gaussian Mixture Regression: Theory and Applications

Explores Gaussian mixture regression and overfitting with multiple Gauss functions.

Marginal Models: Interpretation and Application

Explores marginal models in modern regression, emphasizing interpretation and application in statistical analysis.

Model Diagnostics: Outliers, Leverage, and Influential Observations

Explores outliers, leverage, and influential observations in statistical models, including methods for detection and assessment.