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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? …
R. Cox's user avatar
  • 179
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 …
R. Cox's user avatar
  • 179
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 …
R. Cox's user avatar
  • 179
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. …
R. Cox's user avatar
  • 179
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. …
R. Cox's user avatar
  • 179
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? …
R. Cox's user avatar
  • 179
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
R. Cox's user avatar
  • 179