Implementing Bootstrap Methods in R

سرفصل های دوره

This course focuses on conceptually understanding and robustly implementing various Bootstrap methods in estimation, including correctly using the non-parametric bootstrap, extending the bootstrap method to a Bayesian world, and more.

1. Course Overview

1. Course Overview

2. Getting Started with Bootstrapping in R

01. Version Check

02. Prerequisites and Course Outline

03. Sample Statistics and Confidence Intervals

04. Normally Distributed Data - Estimating Mean

07. Implications of the Central Limit Theorem

09. Demo - The Central Limit Theorem on Real Data

10. Drawbacks of Conventional Approaches

11. Introducing Bootstrapping

12. Bootstrapped Confidence Intervals

3. Implementing Bootstrap Methods for Summary Statistics

4. Demo - Bootstrapping Multiple Statistics

5. Introducing the Bayesian Bootstrap

6. Bayesian Bootstrap Algorithm

7. Demo - Performing Bayesian Bootstrapping

8. Introducing the Smoothed Bootstrap

9. Demo - Smoothed Bootstrap

4. Implementing Bootstrap Methods for Regression Models

1. Introducing Linear Regression

2. Regression Metrics and Bootstrapping

3. Case Resampling and Residual Resampling

7. Summary and Further Study

139,000 تومان

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تولید کننده: PluralSight