Estimating residuals bootstrap in R

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SUMMARY

The discussion focuses on estimating the residual standard error using bootstrap methods in R, specifically with a linear model defined as m1 <- lm(y ~ x + I(x^2)). The user seeks guidance on applying bootstrap techniques to derive residuals, noting the challenge of dependent error terms. Key insights include the use of the sample function in R for resampling, which is essential for implementing the bootstrap method effectively.

PREREQUISITES
  • Understanding of linear regression models in R
  • Familiarity with bootstrap resampling techniques
  • Knowledge of R programming, specifically the lm function
  • Basic statistics concepts, particularly residual analysis
NEXT STEPS
  • Research how to implement bootstrap methods in R using the boot package
  • Learn about the sample function in R for resampling
  • Explore techniques for analyzing residuals in linear models
  • Study the implications of dependent error terms in regression analysis
USEFUL FOR

Statisticians, data analysts, and R programmers interested in advanced regression techniques and bootstrap methods for estimating residuals.

deanac
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Hello, I am trying to predict the residual standard error using bootstrap since they error terms are not independent. So far, I have found a linear model:
m1<-lm(y~x+I(x^2))
but I'm not sure how to use the boot strap to find the residuals.
I am having trouble finding any information online, so I would really appreciate any input.
Thank you!
 
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deanac said:
Hello, I am trying to predict the residual standard error using bootstrap since they error terms are not independent. So far, I have found a linear model:
m1<-lm(y~x+I(x^2))
but I'm not sure how to use the boot strap to find the residuals.
I am having trouble finding any information online, so I would really appreciate any input.
Thank you!

Bootstraping is very well explained here: http://en.wikipedia.org/wiki/Bootstrapping_(statistics)

in R you have to use the instruction sample for the resampling part of the method.
 

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