Statistical Estimation Methods

Related lectures (29)

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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.