Lectures related to Semiparametric model

Statistical Learning Models: Risk and Empirical Risk Minimization

Covers statistical learning models, risk minimization, and empirical risk minimization with examples of maximum-likelihood estimators.

Semiparametric Inference for Missing-Not-at-Random Data

Explores semiparametric inference for missing-not-at-random data, addressing challenges in statistical analysis and proposing a doubly-robust estimator.

Statistical Models: Sampling and Hypothesis Testing

Explores statistical models, sampling distributions, and hypothesis testing using real-world examples.

Size Distribution of Drops and Ice Crystals

Explores the statistical summary of drop properties and the significance of drop size distribution in cloud and precipitation formation.

Kernel Density Estimation: Bandwidth Selection and Curse of Dimensionality

Covers Kernel Density Estimation focusing on bandwidth selection, curse of dimensionality, bias-variance tradeoff, and parametric vs nonparametric models.

Statistical Theory: Fundamentals

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

Signal Models and Methods: Parametric vs Nonparametric

Provides an overview of signal models and methods in statistical signal processing.

Bivariate Maxima: Parametric Models and Distributions

Explores bivariate maxima, parametric models, distributions, and dependence functions in statistics.

Multivariate Extremes: Applications and Dependence

Explores multivariate extremes, including overwhelming sea defenses and heat waves.

Special Families of Models

Explores completeness, minimal sufficiency, and special statistical models, focusing on exponential and transformation families.

Parametric Models: Estimation and Optimization

Explores parametric models, estimation techniques, regression models, and score-based classifiers in data analysis.

Survival Analysis: Cox Proportional Hazards Model

Introduces the Cox proportional hazards model for survival analysis, covering estimation, assessment of assumptions, and interpretation using residuals.

Statistical Inference: Exponential Families and Likelihoods

Explores exponential families, likelihood functions, and model regularity in statistical inference.

Eliminating Nuisance Parameters: Lemmas in Statistical Inference

Explores the elimination of nuisance parameters in statistical models using Lemmas 14 and 15.

Statistical Estimation: Gaussian Linear Model

Delves into statistical estimation, highlighting the Gaussian linear model and the limitations of ML estimators.

Statistical Inference: Model Selection and Nuisance Parameters

Covers model selection, nuisance parameters, and higher-order inference methods in statistical inference.

Eliminating Nuisance Parameters: Statistical Inference

Covers the elimination of nuisance parameters in statistical inference using Lemmas 14 and 15.

Parametric Models: Mathematics of Data

Explores parametric models in data analysis, covering regression estimators, optimization problems, and statistical models.

Parametric Models: Regression Estimators and Optimization

Covers parametric models, regression estimators, and optimization in statistical modeling.