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Introduces statistical inference concepts, focusing on parameter estimation, unbiased estimators, and mean estimation using independent random variables.
Explores the Decision Theory Framework in Statistical Theory, viewing statistics as a random game with key concepts like admissibility, minimax rules, and Bayes rules.
Explores VaR estimation, confidence intervals, backtesting, multivariate distributions, and normality testing through QQ plots and the Jarque-Bera statistic.
Explores constructing confidence regions, inverting hypothesis tests, and the pivotal method, emphasizing the importance of likelihood methods in statistical inference.
Covers the stochastic properties of time series, stationarity, autocovariance, special stochastic processes, spectral density, digital filters, estimation techniques, model checking, forecasting, and advanced models.
