Lectures related to Parameter estimation

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

Introduces linear regression concepts, from X-bands creation to slope estimator properties and tests.

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Explores linear regression and the least squares method to minimize the loss function for the best fit.

Explores non-linear regression models, likelihood estimation, model fitting, and confidence intervals.

Explores estimation, forecasting, and model comparison in time series analysis using real data examples to motivate the study.

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

Covers statistical estimation methods, including maximum likelihood and Bayesian estimation.

Explores the application of linear algebra in data science, covering variance reduction, model distribution theory, and maximum likelihood estimates.

Covers linear regression for estimating train speed using least squares and regularization.

Covers parametric estimation in time series analysis, including integrated processes and seasonal modeling.

Explores the IRLS algorithm for weighted least squares estimation in GLM.

Explores the statistical justification of Least Squares and Generalized Linear Models.

Covers the Stochastic Block Model and its application in community detection, exploring its mathematical formulation and challenges.

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

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

Explores linear regression with a focus on maximum likelihood estimation.

Explores linear regression using the method of least squares to fit data points with the equation y = ax + b.

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.