Lectures related to Quantile | EPFL Graph Search

Quantile Regression: Linear Optimization

Covers quantile regression, focusing on linear optimization for predicting outputs and discussing sensitivity to outliers, problem formulation, and practical implementation.

Quantiles, Sampling, Histogram Density

Explores quantiles, sampling, and histogram density for understanding distributions and constructing confidence intervals.

ECDF and Quantiles

Covers ECDF, quantiles, mean, median, and box plots for dataset analysis.

Extreme Values: Applications and Probability Framework

Explores extreme values in random variables, applications in environmental factors, reliability modeling, block maxima distribution, and the Generalized Extreme Value distribution.

Estimating Random Variables

Covers the estimation of random variables, including expectations, variances, and quantiles.

Statistical Estimation

Covers statistical estimation methods and explores practical examples of parameter estimation for different distributions.

Optimality in Statistical Inference

Delves into the duality between confidence intervals and hypothesis tests, emphasizing the importance of precision and accuracy in estimation.

Statistical Analysis of Extremes: Risk Measures & Inference

Explores risk measures, inference techniques, and statistical analysis of extreme values.

Estimating Extreme Quantiles

Covers the estimation of extreme quantiles using empirical quantiles and sample data.

Statistical Inference: Confidence Intervals

Covers the construction of approximate confidence intervals using the central limit theorem for large sample sizes.

Central Limit Theorem: Properties and Applications

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.

Central Limit Theorem: Illustration and Applications

Explores the Central Limit Theorem and its applications in statistical analysis.

Normal Distribution: Properties and Calculations

Covers the normal distribution, including its properties and calculations.

Types of Variables and Multinomial Distribution

Introduces types of variables, multinomial distribution, data characteristics, shapes of densities, correlation, and data visualization methods.

Exploratory Statistics: Data Analysis Fundamentals

Covers the basics of exploratory statistics, including variables, quantiles, central tendency, dispersion, outliers, and robustness.

Data Analysis: Correlation Measures

Covers the basics of data analysis, focusing on statistical concepts and correlation measures.

Analyse GPS: Variance and Deviation

Covers the analysis of GPS data, including true value comparison and deviation analysis.

Thematic Attributes and Classification

Covers statistical thematic mapping, types of maps, discretization methods, and proportional symbols in maps.

Quantiles and Distributions

Explores quantiles in distribution functions and calculating expectations and variances.

Normal Distribution: Properties and Calculations

Covers properties and calculations related to the normal distribution, including probabilities and quantiles.