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The bootstrap is a resampling method to estimate the sampling distribution of a statistic.
1
vote
2
answers
596
views
Confidence Intervals of the Positive Predictive Value: Adjusting my Bootstrap
I can predict confidence intervals and the distribution of PPV with a bootstrap; that is by sampling from G with replacement. But consider the case where I get a PPV of 1. … How can I adjust my bootstrap to take account of the possibility (it is highly likely) that my sample is not perfectly representative of the total population please? …
0
votes
1
answer
86
views
Hypothesis Testing with a Bootstrap
data.B==1))]))
print('0 0',len(data[((data.A==0)&(data.B==0))]))
print('')
# Results
Lower = {}
Media = {}
Upper = {}
# Control Parameters
Runs_Max = 1000
Runs = range(Runs_Max)
BS = len(data)
print('bootstrap … size: ',BS)
# Results
I_R = []
for R in Runs:
# Bootstrap
BooP = data.sample(BS, replace=True)
# Data
X_11 = len(BooP[((BooP.A==1)&(BooP.B==1))])
X_10 = len(BooP …
0
votes
Accepted
Confidence Intervals of the Positive Predictive Value: Adjusting my Bootstrap
One wrong answer:
Is it ok if I rephrase the question please, using a different example? Consider my ten thousand friends. They each toss a coin. It's the same coin. I know nothing aboout coins. So I …
1
vote
Bootstrap vs Wilson score confidence interval
Interval estimation for a
binomial proportion: a bootstrap approach. Journal of
Statistical Computation and Simulation, 78(12), pp.1251-1265. …
1
vote
Hypothesis Testing with a Bootstrap
The proportion of bootstrap samples for which pB<pA is however zero and this strongly indicates that pB<pA. …
0
votes
1
answer
327
views
Coverage of a Bootstrap Confidence Interval for a Change in a Binomial Proportion
How can I estimate the coverage of a bootstrap confidence interval for a change in a binomial proportion please? … technique defined by the Python code below.
1000 runs
bootstrap size = 28
This gave an estimate and a 95% CI of I:
I = 55% [14%, 150%]
How can I get the coverage of that CI please? …
0
votes
Accepted
Coverage of a Bootstrap Confidence Interval for a Change in a Binomial Proportion
]*li[3]})
# Results
Lower = {}
Media = {}
Upper = {}
# Results
I_R = []
for R in range(runs):
# Bootstrap …