Lectures related to Statistical Estimation Methods

Explores the application of linear algebra in data science, covering variance reduction, model distribution theory, and maximum likelihood estimates.

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Covers regression with exponential family responses using Generalised Linear Models.

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Introduces multivariate statistics, covering normal distribution properties and characteristic functions.

Covers various probability distributions and their applications in real-world scenarios.

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.

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

Covers the theory of sampling, focusing on statistics for mathematicians.

Introduces continuous random variables and their probability distributions, emphasizing their applications in statistics and data science.

Covers the properties of the exponential family and the estimation of parameters.

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Covers continuous random variables, probability density functions, and distributions, with practical examples.

Covers fundamental statistics concepts, including estimation theory, distributions, and the law of large numbers, with practical examples.