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Most people are just used to Monte Carlo versions of the bootstrap, so their hair may stand up on their heads when they would hear about deterministic bootstrap, but one should not forget that the distant … goal is the complete bootstrap, and whatever gets us closer to it is helpful. …

that that theoretical bootstrap in practice, anyway). … As such, a Monte Carlo implementation of the bootstrap is unfixable, and you have to use a different bootstrap scheme. …

This is the bootstrap analogy principle. … So the bootstrap analogue of the (sampling + systematic) deviation $\hat\theta - \theta_0$, the quantity of your central interest, is the deviation of the bootstrap replicate $\hat\theta^*$ from what is …

You can probably devise a wild-bootstrap-like routine that would produce $y_i^*=1$ with probability $\hat p_i$ and $y_i^*=0$ with probability $1-\hat p_i$; this would essentially be a parametric bootstrap … Moreover, generalizations to cluster wild bootstrap are absolutely weird: the wild bootstrap version would be to either retain all the exiting patterns of 0 and 1 within the cluster, or flipping all of …

The bootstrap is an asymptotic technique that assumes large sample sizes. Deviations from the expected asymptotic normal distributions are driven by the higher order moments of your data. … Bottomline: while the wheels of the bootstrap as a computational method do churn and generate some answers, the results are not statistically interpretable. …

bootstrap of residuals, even if you have fitted a heteroskedasticty model to it). … For a general discussion of issues you could face with the bootstrap, see Canty, Davison, Hinkley and Ventura (2006). Bootstrap diagnostics and remedies. …

See Rao and Wu 1998 for the basic motivation, and if that's not accessible, Preston 2009 or my Stata paper and code for the survey bootstrap weights. … Bootstrap can be used to correct for the bias (it's not very frequently used for this purpose, but should be for survey data like your situation). I would not use the percentile method here. …

Marginally longer answer: A single vector of weights tells you nothing about: Stratification Clustering Calibration variables A proper bootstrap scheme would involve the following (I can refer to my … Take a bootstrap sample (with replacement) of clusters within each stratum, independently across strata. Re-calibrate the sample to the same population totals that were used for the main weights. …

This is the question mark in the lower left of the diagram, and this is the question the bootstrap tries to answer. … The above conditions are spelled out it utmost technicality in Hall's The Bootstrap and Edgeworth Expansion (1992) book. …

R's survey package can create the bootstrap weights that account for these aspects of the survey; I doubt boot package does this properly. …

I don't think applying a particular model such as fixed effects changes things much, but, IMO, the residual bootstrap makes a lot of strong assumptions (the residuals are i.i.d., the model is correctly … There's been some econometrics literature on wild cluster bootstrap. …

I have written cluster bootstrap code in Stata, see http://www.stata-journal.com/article.html?article=st0187. …

The traditional bootstrap fails when the parameter is on the boundary of the parameter space, as in the mixed model example of estimating the variance of random effects. … You'd have to do something smarter, which leads you to the parametric bootstrap where you'd have to effectively recreate your model under the null with no random effects -- see this post for additional …

It seems to me that your question is ill-posed (which was pointed out by Aniko already). If you know the mean, your uncertainty about it is zero, so the confidence interval should have zero length. …

Now, the technique in question is the bootstrap, which involves Monte Carlo simulation within it (unless, as whuber demonstrated, you are asked to perform the exact bootstrap, which is highly unlikely) … The bootstrap CI may show better performance (say, 6% and 4%). …