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Related lectures (32)

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Modern Regression: Spring Barley Data

Covers iterative weighted least squares, Poisson regression, and Bayesian analysis of spring barley data using mixed models.

Inference: Poisson Regression

Covers iterative weighted least squares, model checking, Poisson regression, and fitting multinomial models using Poisson errors.

Marginal Models: Interpretation and Application

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

Modern Regression: Overdispersion and Model Assessment

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

Likelihood Inference

Covers iterative weighted least squares, Poisson regression, mixed models, and likelihood ratio statistic.

Regression Methods: Spline Smoothing

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

Optimization in Statistics and Machine Learning: Maximum Likelihood Estimation

Explores maximum likelihood estimation, logistic regression, covariance estimation, and support vector machines for classification problems.

Kalman Filter: Linearized Models

Explains linearized and non-linear Kalman Filter models and the use of Jacobians.

Debiased Whittle Likelihood: Time Series and Spatial Data

Explores the Debiased Whittle likelihood for time series and spatial data, focusing on fitting spectral density to the periodogram for better predictions and parameter estimation.

Nested Model Selection

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