Concept

Linear probability model

Related lectures (30)

Covers regression diagnostics for linear models, emphasizing the importance of checking assumptions and identifying outliers and influential observations.

Assessing Significance and Fit

Covers confidence intervals, R2, and examples on cement heat evolution and car horsepower-MPG relationships.

Linear Models: Introduction

Introduces linear models, regression, Gaussian distribution, linearity, and model generalization.

Multicollinearity: Dangers and Remedies

Explores the dangers of multicollinearity in linear models and discusses diagnostic methods and remedies.

Provable Benefits of Overparameterization in Model Compression

Explores the provable benefits of overparameterization in model compression, emphasizing the efficiency of deep neural networks and the importance of retraining for improved performance.

Algebraic statistics: tables and networks

Explores algebraic statistics, log-linear models, and network testing for model goodness of fit.

Ridge Regression: Penalised Least Squares

Explores Ridge Regression for handling multicollinearity and the LASSO method for model selection.

Nested Model Selection

Explores nested model selection in linear models, comparing models through sums of squares and ANOVA, with practical examples.

Linear Models: Least Squares

Explores linear models, least squares, Gaussian vectors, and model selection methods.

Likelihood Estimation and Least Squares

Introduces simple and multiple normal linear regression, and maximum likelihood estimation with practical examples.

Regularization in Machine Learning

Introduces regularization techniques to prevent overfitting in machine learning models.

Geometry and Least Squares

Discusses the geometry of least squares, exploring row and column perspectives, hyperplanes, projections, residuals, and unique vectors.

Linear Models and Overfitting

Explores linear models, overfitting, and the importance of feature expansion and adding more data to reduce overfitting.

Non-Negative Definite Matrices and Covariance Matrices

Covers non-negative definite matrices, covariance matrices, and Principal Component Analysis for optimal dimension reduction.

Experimental Design in Genomic Data Analysis

Emphasizes experimental design in genomic data analysis, addressing technical variability, batch effects, and statistical solutions.

Regularization Techniques

Explores regularization in linear models, including Ridge Regression and the Lasso, analytical solutions, and polynomial ridge regression.

Optimality and Asymptotics

Explores the optimality of the Least Squares Estimator and its large sample distribution.

MLE Applications: Binary Choice Models

Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.

Neural Tangent Kernel: Generalization in Deep Learning

Explores the neural tangent kernel in deep learning, analyzing generalization and network behavior.

Linear and Quadratic Models of Scheffé

Explores linear and quadratic models of Scheffé in mixing plans and ternary schemes, emphasizing constraints and representations.