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

05. Normally Distributed Data - Calculating Confidence Intervals

06. Data with Any Distribution - Estimating Mean and Confidence Intervals

07. Implications of the Central Limit Theorem

08. Demo - The Central Limit Theorem with Different Distributions

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

1. Demo - Bootstrap Statistics and Sample Statistics

2. Demo - Performing Bootstrapping on Real Data

3. Demo - Performing Bootstrapping Using the Boot Method

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

4. Demo - Estimating R2 of Regression Models Using Bootstrapping

5. Demo - Estimating Coefficients Using Bootstrapping

6. Demo - Case Resampling and Residual Resampling Using the Boot() Method

7. Summary and Further Study

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تولید کننده: PluralSight