Modeling Elasticity and Coefficients

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Applying the learning bound to kernel regression

Discusses the application of the main theorem to least square regression in a RKHS, focusing on LR of the Rademacher bound and Lipschitz constant.

Error Estimation in LHS

Covers error estimation in Latin Hypercube Sampling, emphasizing the importance of accurate variance estimation.

Inference and Mixed Models

Covers point estimation, confidence intervals, and hypothesis testing for smooth functions using mixed models and spline smoothing.

Statistical Inference for Bandit Data

Explores statistical inference for bandit data, focusing on personalized treatment actions and challenges of standard estimators.

Regression Analysis: Multivariate Data Modeling

Introduces regression analysis for multivariate data modeling, covering matrix algebra, interpretation of coefficients, and test intervals.

Estimation and Confidence Intervals

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Linear Regression: Ozone Data Analysis

Explores linear regression analysis of ozone data using statistical models.

Regression: Simple and Multiple Linear

Covers simple and multiple linear regression, including least squares estimation and model diagnostics.

Linear Regression Basics

Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.

Elasticity: Constitutive Modelling in Geomechanics

Explores constitutive equations in geomechanics, stress-strain behavior, and practical applications in engineering.