Spectral Estimation in Time Series

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Parameter Estimation of SDEs with Linear Response Theory

Explores parameter estimation of SDEs using Linear Response Theory and covers challenges, examples, algorithms, and convergence.

Estimation & Spectral Analysis in Time Series

Covers the estimation of time series models and spectral analysis in depth.

Probability and Estimation in Statistics

Introduces probability, estimation methods, linear models, testing, and advanced regression techniques.

Model Specification in Time Series

Covers the identification and model specification in time series analysis, including AR models and least squares estimation.

The Stein Phenomenon and Superefficiency

Explores the Stein Phenomenon, showcasing the benefits of bias in high-dimensional statistics and the superiority of the James-Stein Estimator over the Maximum Likelihood Estimator.

Basic Principles of Point Estimation

Explores the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle in point estimation.

Time Series: Structural Modelling and Kalman Filter

Covers structural modelling, Kalman Filter, stationarity, estimation methods, forecasting, and ARCH models in time series.

Signal Processing Fundamentals

Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.

Box-Jenkins Methodology: Building Time Series Models

Covers the Box-Jenkins methodology for building time series models, including model identification, variance calculations, and model diagnostics.

Spectral Analysis: Time Series

Explores spectral analysis in time series, focusing on spectral density functions and integrated spectra.