Generalized Linear Models

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Related lectures (53)

Convergence in Probability Log in to Mediaspace to watch this video

Explores convergence in probability, concentration inequalities, laws of large numbers, and properties of distributions.

Linear Regression: Ozone Data Analysis Log in to Mediaspace to watch this video

Explores linear regression analysis of ozone data using statistical models.

Law of Large Numbers: Strong Convergence Log in to Mediaspace to watch this video

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

Normal Distribution: Properties and Calculations Log in to Mediaspace to watch this video

Covers the normal distribution, including its properties and calculations.

Elements of Statistics: Memorylessness, Stationary Processes, Estimation using MLE Log in to Mediaspace to watch this video

Explores memorylessness in distributions, stationary processes, and estimation using MLE.

Elements of Statistics: Probability and Random Variables Log in to Mediaspace to watch this video

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Probability Fundamentals Log in to Mediaspace to watch this video

Introduces fundamental probability concepts, including events, complements, conditional probability, and random variables.

Conditional Probability: Definition and Examples Log in to Mediaspace to watch this video

Explains the calculation of conditional probability with definitions and examples.

Applications of Quantum Science: Densities and Statistics Log in to Mediaspace to watch this video

Covers the applications of densities and statistics in quantum science, focusing on binomial and Poisson distributions.

Central Limit Theorem Log in to Mediaspace to watch this video

Covers the central limit theorem, showing how random processes converge to a normal distribution.

Statistical Theory: Maximum Likelihood Estimation Log in to Mediaspace to watch this video

Explores the consistency and asymptotic properties of the Maximum Likelihood Estimator, including challenges in proving its consistency and constructing MLE-like estimators.

Conditional Probability: Understanding Events and Their Relationships Log in to Mediaspace to watch this video

Covers conditional probability, illustrating how events influence each other's probabilities and its applications in real-world scenarios.

Binomial and Poisson Mass Functions Log in to Mediaspace to watch this video

Explores binomial and Poisson mass functions, calculating probabilities and discussing distribution functions of random variables.

Generalized Linear Models: A Brief Review Log in to Mediaspace to watch this video

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

Probability Inequalities Log in to Mediaspace to watch this video

Explores probability inequalities, convergence types, and moment generating functions for distribution approximation.

Stochastic Models for Communications Log in to Mediaspace to watch this video

Covers random vectors, joint probability density, independent random variables, functions of two random variables, and Gaussian random variables.

Statistical Analysis: Hypothesis Testing and Distribution Models Log in to Mediaspace to watch this video

Covers statistical analysis techniques, hypothesis testing, and distribution models using R software.

Probability Laws: Random Variables Log in to Mediaspace to watch this video

Covers the definition of probability laws for random variables and examples of discrete and continuous variables.

Regression Methods: Model Building and Inference Log in to Mediaspace to watch this video

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

Probability Distributions: Moments and Transformations Log in to Mediaspace to watch this video

Explores the bijection between distribution functions and moment generating functions.

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