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When forming the bootstrap estimates, you have the line of code: pred.boot[i] <- ifelse (mean(res.boot$t[, i] == "pos") >= 0.5, "pos", "neg") which doesn't work, as res.boot$t is not a matrix of factors that map to "pos" and "neg", but instead a matrix of integers taking on the values of $1$ or $2$. Consequently, the equality is always false, the ...


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I guess you refer to the bootstrap estimators of an error rate as suggested in B. Efron: Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation. Journal of the American Statistical Association 78, pp. 316-331 (1983) The article seems to be inaccessible behind a paywall, but Andrew Webb ("Statistical Pattern Recognition", 2nd ed....


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The aim of Willemain et al.'s (2004) bootstrap is to estimate the entire probability distribution of total demand over the lead time. So if you want a safety stock that satisfies total demand in 95% of cases, just take the 95% quantile of this (bootstrap) estimated distribution. Note that there are other definitions of service levels, many of which are ...


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A (non-parametric) bootstrap sample is just sampling from the empirical distribution. So when you do what is most commonly called bootstrapping you are just simulating from an estimate of the population/distribution. Parametric bootstrap involves sampling from a distribution that you believe represents the population or random procedure, your described ...


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The function according to the vignetter say "The first argument passed will always be the original data. The second will be a vector of indices, frequencies or weights which define the bootstrap sample." Putting it into a function below, you define data as the data and idx and the so called index to sample with replacement from: library(boot) func = ...


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It sounds like your colleague wants you to demonstrate that your method works better as you take more iterations, which is a perfectly reasonable request. If you want to demonstrate this by simulation, this would entail generating a large number of "searches" that your algorithm generates for values $i=1,2,...,p$, and showing that the outcomes tend to be ...


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I'm no expert but I'll try to give a shot at your question. If I understand it correctly, you have a sample of datapoints d but you don't know the parameters of the distribution that generated it. You want to know how extreme the value x is in that sample. So you have: set.seed(1234) d <- rbeta(100, 1, 10) # You don't know the parameters x <- 0.01 To ...


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Welcome to our site! But your question is very broad and unclear, see below. In regression, we do not care about the marginal distributions of neither the dependent nor predictor variables. We care about the conditional distribution of the dependent variable (given the regressors), that is, of the error term. In regression models we condition on $x$, that ...


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