Bootstrapping (statistics)

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Related lectures (32)

Page 3 of 4

Explores correlated sampling, weight functions, Markovian chains, ergodicity, and walkers.

Stochastic Blockmodel Estimation

Explores Stochastic Blockmodel estimation, spectral clustering, network modularity, Laplacian matrix, and k-means clustering.

Distributionally Robust Portfolio Optimization

Explores distributionally robust portfolio optimization and compares different estimation approaches and methods for portfolio evaluation.

Interval Estimation

Covers the construction of confidence intervals for a normal distribution with unknown mean and variance.

Model Selection: Evaluation and Generalization

Explores model selection, evaluation, and generalization in machine learning, emphasizing unbiased performance estimation and the risks of over-learning.

Acceptance-Rejection Methods: Advanced Techniques

Explores advanced Acceptance-Rejection methods, sampling from normal distribution, and multivariate random variable generation.

Monte Carlo Integration: Correlated Sampling

Covers Monte Carlo integration through correlated sampling and error scaling for grid integration.

Stochastic Simulations: Ergodicity and Estimators

Explores geometric ergodicity in Markov chains and estimators' bias and variance, highlighting efficiency loss quantification.

Errors in Sampling: Correlation and Time Analysis

Explores errors in sampling, correlation functions, time analysis, and blocking techniques.

Statistical Mediation and Moderation

Explores statistical mediation, moderation, ANOVA, and hypothesis testing.