Concept

Nonparametric statistics

Related lectures (32)

Probability Distributions in Environmental Studies

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

Nonparametric and Bayesian Statistics

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

Hypothesis Testing: Statistics Overview

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

Nonparametric Statistics: Estimation and Optimization

Explores nonparametric statistics, covering estimation methods and the bias-variance tradeoff.

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.

Spectral & Parametric Estimation: Time Series

Covers spectral estimation techniques like tapering and parametric estimation, emphasizing the importance of AR models and Whittle likelihood in time series analysis.

Nonparametric Regression: Kernel-Based Estimation

Covers nonparametric regression using kernel-based estimation techniques to model complex relationships between variables.

Statistical Learning Models: Risk and Empirical Risk Minimization

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

Nonparametric Statistics: Bayesian Approach

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

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 Models: Sampling and Hypothesis Testing

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

Optimal Estimation and Bias in Statistics

Delves into optimal estimation, bias role in finite samples, and the delicate tradeoff between bias and variance in statistical estimation.

Nonparametric Regression

Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.

Statistical Signal Processing Tools

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

Extreme Region Likelihood Estimation

Covers extreme region likelihood estimation, model complexity, oceanographic data modeling, and threshold likelihood estimation.

Understanding Statistics & Experimental Design

Explores t-tests, confidence intervals, ANOVA, and hypothesis testing in statistics, emphasizing the importance of avoiding false discoveries and understanding the logic behind statistical tests.

Linear Independence: Covariance and Statistical Dependence

Explores covariance, statistical dependence, education-fertility relationship, hypothesis testing, and comparison statistics for continuous outcomes.

Hypothesis Testing in Statistics

Delves into hypothesis testing, covering test statistics, critical regions, power functions, p-values, multiple testing, and non-parametric statistics.

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.