What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum

DOI10.1080/00031305.2015.1089789OpenAlexW1909800943WikidataQ38873305 ScholiaQ38873305MaRDI QIDQ5885412

T. C. Hesterberg

Publication date: 3 April 2023

Published in: The American Statistician (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1411.5279

zbMATH Keywords

confidence intervalsbiasteachingsampling distributionstandard errorstatistical concepts

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (13)

Estimating Desired Sample Size for Simple Random Sampling of a Skewed Population ⋮ A Guide to Teaching Data Science ⋮ Teaching the Next Generation of Statistics Students to “Think With Data”: Special Issue on Statistics and the Undergraduate Curriculum ⋮ Bootstrap based multi-step ahead joint forecast densities for financial interval-valued time series ⋮ Spatial bootstrapped microeconometrics: Forecasting for out‐of‐sample geo‐locations in big data ⋮ Statistical inferences on nonconstant relative potency with quantal response data ⋮ Applications of the Fractional-Random-Weight Bootstrap ⋮ Causality in extremes of time series ⋮ A quantitative insight into the dependence dynamics of the Kilauea and Mauna Loa volcanoes, Hawaii ⋮ (Un)Conditional Sample Generation Based on Distribution Element Trees ⋮ Bootstrap methods for judgment post stratification ⋮ The designed bootstrap for causal inference in big observational data ⋮ Key Attributes of a Modern Statistical Computing Tool

Uses Software

• boot
• mosaic
• bootstrap

Cites Work

• On smoothing and the bootstrap
• The bootstrap: To smooth or not to smooth?
• Nonparametric standard errors and confidence intervals
• The bootstrap and Edgeworth expansion
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum