Estimating R: Moments and Covariance

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Explores dependence in random vectors, covering joint density, conditional independence, covariance, and moment generating functions.

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

Elements of Statistics: Probability and Random Variables

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

Maximum Likelihood Estimation: Multivariate Statistics

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Multivariate Statistics: Wishart and Hotelling T²

Explores the Wishart distribution, properties of Wishart matrices, and the Hotelling T² distribution, including the two-sample Hotelling T² statistic.

Elements of Statistics: Probability, Distributions, and Estimation

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

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

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

Multivariate Statistics: Normal Distribution

Covers the multivariate normal distribution, properties, and sampling methods.