Bootstrapping, how is it different between one iteration with large sample size and many iterations?2019 Community Moderator ElectionCalculating standard deviation of samples with boostrapping in RHow to see resampled data after BOOTSTRAPBootstrap and sample meanCan Random.nextgaussian() sample values from a distribution with different mean and standard deviation?How to add bootstrap CI into this function in REfficient way to sample a large array many times with NumPy?Does it make sense to increase sample count using bootstrapping? Then, how can achieve it?how to do bootstrap simulation for gpd distributionSigma of large sampleSelect a sample at random and use it to generate 1000 bootstrap samples
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Bootstrapping, how is it different between one iteration with large sample size and many iterations?
2019 Community Moderator ElectionCalculating standard deviation of samples with boostrapping in RHow to see resampled data after BOOTSTRAPBootstrap and sample meanCan Random.nextgaussian() sample values from a distribution with different mean and standard deviation?How to add bootstrap CI into this function in REfficient way to sample a large array many times with NumPy?Does it make sense to increase sample count using bootstrapping? Then, how can achieve it?how to do bootstrap simulation for gpd distributionSigma of large sampleSelect a sample at random and use it to generate 1000 bootstrap samples
I'm learning how to do the bootstrapping. I know that it is a resampling technique with replacement. From what I understand, the way to do it is to set a large number of iterations, ITER. Then in each iteration, you resample with the sample size N (typically equal to the original sample size). Then, you calculate statistics (mean, standard deviation) from the resampled sample. Repeat this with the given number of iteration ITER. Then, we can construct the sample mean distribution.
So, I wonder, what would be the difference between performing ITER * N bootstrapping and 1 * (ITER * N)?
statistics statistics-bootstrap
add a comment |
I'm learning how to do the bootstrapping. I know that it is a resampling technique with replacement. From what I understand, the way to do it is to set a large number of iterations, ITER. Then in each iteration, you resample with the sample size N (typically equal to the original sample size). Then, you calculate statistics (mean, standard deviation) from the resampled sample. Repeat this with the given number of iteration ITER. Then, we can construct the sample mean distribution.
So, I wonder, what would be the difference between performing ITER * N bootstrapping and 1 * (ITER * N)?
statistics statistics-bootstrap
add a comment |
I'm learning how to do the bootstrapping. I know that it is a resampling technique with replacement. From what I understand, the way to do it is to set a large number of iterations, ITER. Then in each iteration, you resample with the sample size N (typically equal to the original sample size). Then, you calculate statistics (mean, standard deviation) from the resampled sample. Repeat this with the given number of iteration ITER. Then, we can construct the sample mean distribution.
So, I wonder, what would be the difference between performing ITER * N bootstrapping and 1 * (ITER * N)?
statistics statistics-bootstrap
I'm learning how to do the bootstrapping. I know that it is a resampling technique with replacement. From what I understand, the way to do it is to set a large number of iterations, ITER. Then in each iteration, you resample with the sample size N (typically equal to the original sample size). Then, you calculate statistics (mean, standard deviation) from the resampled sample. Repeat this with the given number of iteration ITER. Then, we can construct the sample mean distribution.
So, I wonder, what would be the difference between performing ITER * N bootstrapping and 1 * (ITER * N)?
statistics statistics-bootstrap
statistics statistics-bootstrap
edited Mar 8 at 0:39
ottomeister
2,68421218
2,68421218
asked Mar 7 at 17:13
Kornpob BhirombhakdiKornpob Bhirombhakdi
1094
1094
add a comment |
add a comment |
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