Multivariate Statistics: Introduction and Methods

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Page 2 of 4

Explores copulas in multivariate statistics, covering properties, fallacies, and applications in modeling dependence structures.

Introduces Principal Component Analysis, focusing on maximizing variance in linear combinations to summarize data effectively.

Explores market response functions, flash crashes, correlation estimation, and noise filtering in finance.

Covers random vectors, joint probability, and conditional probability in communication stochastic models.

Covers random vectors, joint distribution, conditional density functions, independence, covariance, correlation, and conditional expectation.

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

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

Covers elliptical distributions, including properties, applications, and risk management implications.

Explores covariance, statistical dependence, education-fertility relationship, hypothesis testing, and comparison statistics for continuous outcomes.

Covers the multivariate normal distribution, moment-generating function, and combinatorics.