2 replaced http://stats.stackexchange.com/ with https://stats.stackexchange.com/
source | link

Will I obtain seriously biased results if I use bootstrapping on a subsample of a larger dataset?

Rather than drawing 100 bootstrap samples from a dataset of 50 million + records, which could hog server resources, I'm thinking of first drawing a 5% random sample, w/ replacement, of records from the master dataset. Then build bootstrapped 95% confidence intervals for several statistics of interest using the 5% sample. So in essence I'll be bootstrapping from a bootstrap sample.

I'm working with health data which is clustered according to provider and episode of care, hence drawing a proper representative 5% sample will require some care. Some earlier advice has pointed me in the right direction ( http://stats.stackexchange.com/questions/43185/proper-bootstrapping-technique-for-clustered-dataProper bootstrapping technique for clustered data? ).

Will I obtain seriously biased results if I use bootstrapping on a subsample of a larger dataset?

Rather than drawing 100 bootstrap samples from a dataset of 50 million + records, which could hog server resources, I'm thinking of first drawing a 5% random sample, w/ replacement, of records from the master dataset. Then build bootstrapped 95% confidence intervals for several statistics of interest using the 5% sample. So in essence I'll be bootstrapping from a bootstrap sample.

I'm working with health data which is clustered according to provider and episode of care, hence drawing a proper representative 5% sample will require some care. Some earlier advice has pointed me in the right direction ( http://stats.stackexchange.com/questions/43185/proper-bootstrapping-technique-for-clustered-data ).

Will I obtain seriously biased results if I use bootstrapping on a subsample of a larger dataset?

Rather than drawing 100 bootstrap samples from a dataset of 50 million + records, which could hog server resources, I'm thinking of first drawing a 5% random sample, w/ replacement, of records from the master dataset. Then build bootstrapped 95% confidence intervals for several statistics of interest using the 5% sample. So in essence I'll be bootstrapping from a bootstrap sample.

I'm working with health data which is clustered according to provider and episode of care, hence drawing a proper representative 5% sample will require some care. Some earlier advice has pointed me in the right direction ( Proper bootstrapping technique for clustered data? ).

    Tweeted twitter.com/#!/StackStats/status/271418176347205632
1
source | link

What are pitfalls of bootstrapping on random sample of master data?

Will I obtain seriously biased results if I use bootstrapping on a subsample of a larger dataset?

Rather than drawing 100 bootstrap samples from a dataset of 50 million + records, which could hog server resources, I'm thinking of first drawing a 5% random sample, w/ replacement, of records from the master dataset. Then build bootstrapped 95% confidence intervals for several statistics of interest using the 5% sample. So in essence I'll be bootstrapping from a bootstrap sample.

I'm working with health data which is clustered according to provider and episode of care, hence drawing a proper representative 5% sample will require some care. Some earlier advice has pointed me in the right direction ( http://stats.stackexchange.com/questions/43185/proper-bootstrapping-technique-for-clustered-data ).