Lectures related to Parametric model

Provides an overview of hypothesis testing, p-values, Wald test, and non-parametric statistics.

Statistical Learning Models: Risk and Empirical Risk Minimization

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

Statistical Theory: Fundamentals

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

Special Families of Models

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

Statistical Signal Processing Tools

Explores statistical signal processing tools for wireless communications, including spectral estimation and signal detection, classification, and adaptive filtering.

Geometry: Introduction to Euclidean Elements

Explores the historical evolution of geometric instruments and the transition to modern CAD software for architectural design.

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.

Statistical Inference: Exponential Families and Likelihoods

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

Statistical Models: Sampling and Hypothesis Testing

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

Parametric Models: Estimation and Optimization

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

Geometric Structures: Stereotomy

Explores traditional stone cutting and assembly techniques, geometric modeling, and the historical development of projective geometry in stereotomy.

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.

Bivariate Maxima: Parametric Models and Distributions

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

Poisson Process Approach

Explores the Poisson process approach in extreme value analysis, emphasizing component-wise transformations and likelihood functions for extreme events.

Signal Models and Methods: Parametric vs Nonparametric

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

Multivariate Extremes: Applications and Dependence

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

Parametric Models: Logistic and Dirichlet Densities

Explores logistic and Dirichlet densities, parametric models, and asymmetric alternatives.

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.

Nonparametric and Bayesian Statistics

Covers nonparametric statistics, kernel density estimation, Bayesian principles, and posterior distribution summarization.