The bootstrap is a resampling method to estimate the sampling distribution of a statistic.

learn more… | top users | synonyms

0
votes
0answers
9 views

Blocked Weighted Bootstrap

Bootstrap is a well known resampling method. But I want to know what is blocked weighted bootstrap sampling? Why we need this?
1
vote
0answers
39 views

lmer() parametric bootstrap testing for fixed effects

I am performing a parametric bootstrap to test whether I need a specific fixed effect in my model or not. I have mainly done this for exercise and I am interested if my procedure so far is correct. ...
1
vote
1answer
29 views

Addressing Non-response in a Convenience Sample

I am studying customer satisfaction in a large hierarchical organization. I plan to administer a voluntary survey to customers across the organization, and need to address non-response in my analysis. ...
0
votes
1answer
20 views

Error Propagation Calculation

I have a few machines that are used to calibrate each other. Machine 1 has is accurate to 0.025% Machine 1 is used to calibrate Machine 2, which has an accuracy of 0.005% Machine 2 is used to ...
1
vote
0answers
28 views

bootstrapping for a parameter estimation

At the moment I'm studying bootstrap and I want to do some examples to get a feeling for it. Suppose I generate the following data: ...
0
votes
1answer
20 views

Bootstrapped t test in SPSS: Why are some p value cells blank?

Using an independent samples t test, I am comparing the performance of 2 groups on an activity, based on the percentage of items correct (the percentage of total items correct, as well as percentage ...
0
votes
0answers
10 views

NPS confidence intervals with bootstrap

I am looking for a method to find confidence intervals for NPS (net promoter score). One method was already suggested here. But I am thinking of using bootstrap. Do you think the following procedure ...
0
votes
1answer
29 views

Bootstrapping t-test for nonparametric data

I have a two heavily skewed distributions and am trying to use bootstrapping to compare their means via the t-test. I am concerned about the appropriateness of using the standard error of the ...
0
votes
0answers
20 views

Please provide an example of when bootstrap has less bias than classically approximated estimates?

The recent question "Why does my bootstrap interval have terrible coverage?" has got me wondering if anybody has some really good examples of distributions in which bootstrapping standard errors ...
1
vote
1answer
722 views

How did Efron imagine the bootstrap?

Do you know Bradley Efron? He's a great man. How did Efron imagine or think about "bootstrap" for the first time?
4
votes
1answer
54 views

Using Univariate ANOVA with non-normally distributed data

If my data are non-normally distributed and I'm conducting a 2x2 ANOVA, what can I do to correct for this problem so I can report the main effect and interaction output appropriately? Only one ...
1
vote
1answer
24 views

Reporting bootstrapped output for an independent samples t test

When reporting results of a bootstrapped independent samples t-test, can I just report the t statistic alongside the bootstrapped p value? Or do I also need to report confidence intervals?
0
votes
0answers
19 views

R: Most efficient way to bootstrap fan charts

I want to bootstrap a timeseries prediction to generate fan charts of marginal prediction intervals. What is the most efficient way to do this? Something like this? ...
17
votes
4answers
388 views

Why does my bootstrap interval have terrible coverage?

I wanted to do a class demonstration where I compare a t-interval to a bootstrap interval and calculate the coverage probability of both. I wanted the data to come from a skewed distribution so I ...
0
votes
0answers
20 views

Bootstrapping to find how many samples need to be sampled

I need to know how many samples need to be sampled to get an allele frequency in a population that is very close to a "true" allele frequency assuming that we know a population size and a true allele ...
1
vote
2answers
66 views

How to compare two non-normally distributed samples with very different sizes? (Mann-Whitney vs Randomization/Bootstrap)

Perhaps this is a very basic question, but I didn't find yet a simple solution for this simple problem: I want to compare two samples (say X and Y) for a continuous variable which is non-normally ...
0
votes
0answers
9 views

Estimating variance of prediction error in bootstrapped training sets with clustered data

I have C clusters with m elements each. I split the C clusters into a large training set D and a test set T. Hence, each element in D and T has m related elements, so its a cluster. I want to ...
1
vote
0answers
23 views

rms validate on models with a predict function such as coxph and glmnet

I would like to use bootstrapping to evaluate models generated by coxph and glmnet. Would that be somehow possible with rms validate ? From the documentation it seems limited to rms functions (cph, ...
3
votes
0answers
29 views

Bootstrap approach for testing differences in interquartile range suitable?

I am investigating differences in two groups of forecast errors (absolute values / unsigned) that both have a strongly right-skewed distributions. Both groups of errors have the same number of ...
1
vote
2answers
52 views

Proportion of Intervals from t-interval method

Here is an excerpt of a book question: The t-interval method assumes that the underlying distribution is approximately normal. If we were to generate 800 samples of sample size 15 from this ...
1
vote
0answers
22 views

Comparison between groups with unequal sample sizes

I have calculated modulation indices (scalar) of neuronal responses for two categories of neurons $x, y$ recorded from two hemispheres $a, b$, with $n_{xa}=120, n_{xb}=80, n_{ya}=20, n_{yb}=20 $. ...
0
votes
0answers
30 views

Cross-validated bootstrap samples

In one of the paper I am writing, I am looking at cross-validation on bootstrap samples. I wrote the following explanation.One of the reviewer wrote that he didn't understand where is the correlation. ...
0
votes
0answers
26 views

multiple regression and bootstrapping

I performed multiple regression and some of the assumptions are violated like normality of the outcome variable. Can I use bootstrap to correct for violated assumptions? Or doing it would be ...
2
votes
1answer
81 views

Advantages of bootstrapping when calculating mean

I'm a statistically incompetent social scientist who is very much out of my depth... I'm using R to analyse a data set, which is a 1% sample of a population showing cancer rates across 20 regions. ...
1
vote
0answers
7 views

Joint vs. marginal prediction intervals for path forecasts (with k-family wise error rate)

I am trying to become comfortable with the bootstrapping of joint prediction regions described in this paper: http://www.nccr-finrisk.uzh.ch/media/pdf/wp/WP748_A3.pdf This calculates the prediction ...
0
votes
1answer
62 views

Is the bootstrap estimate of the mean biased when a single extreme value is in the sample?

My sample includes $n$ random observations, while $n-1$ of these observations are in the range (0-1) there is also one observation that gets very high value. For example, a sample of prices where ...
11
votes
4answers
584 views

Why on average does each bootstrap sample contain roughly two thirds of observations?

I have run across the assertion that each bootstrap sample (or bagged tree) will contain on average approximately $2/3$ of the observations. I understand that the chance of not being selected in any ...
3
votes
0answers
97 views

Two-sample bootstrap?

I have two independent samples of observations. From each sample I produce a statistic. Let's denote these as $\theta_1$ and $\theta_2$. I'd like to test the hypothesis that $H_0: \Theta_1=\Theta_2$, ...
0
votes
0answers
61 views

Bootstrap and var of median

I've 2 different questions: A. Sample with n variables, while $n-1$ variables are in the range (0-1), and one of the variable is very high: $10^{1000}$. If I'll use bootstrap to estimate the mean, ...
0
votes
1answer
79 views

Discrepancy between log likelihood Harrell's C Index and brier score for the evaluation of a Cox regression

I am evaluating a dataset of ~400 subjects and 10 covariates trying to fit a Cox ph model for predicting survival in AML patients. To evaluate the models I am using a bootstrapping procedure of 50 ...
1
vote
1answer
46 views

Reweighting importance-weighted samples in Bayesian bootstrap

Typically, in Bayesian bootstrap, you have samples {$x_1,...,x_n$} of a random variable $X$. Choose $\{p_1,...,p_n\}$ from a Dirichlet distribution, by sorting $\{0,1,u_1,...,u_{n-1}\}$ where $u_i$ ...
0
votes
2answers
105 views

Probability Given Datapoint Does Not Appear in a Bootstrap Sample? [duplicate]

I received this question for a Statistics / Machine Learning assignment and I'd like to you if any of you know the proper answer. If we have n data points, what is the probability that a given data ...
0
votes
0answers
25 views

Confidence interval of metric from empirical distribution

I'm familiar with using boot in R to estimate a confidence interval for VaR. For example, I have a set of monthly returns for an asset, and I want a range on the ...
2
votes
1answer
95 views

Hypothesis testing - Wilcoxon test, bootstrapping, or something else?

A colleague has developed a treatment for to "prevent falls" in cognitively impaired, psychiatric patients. Since this would be very useful treatment in this population, we especially do not want to ...
0
votes
0answers
23 views

Bootstrapping data with only sampling weights given

Suppose you only have these information from a sample data: $X_i$ and $w_i$, $i=1,...,N$, where $w_i$'s are the respective sampling weights(not integers). Is it possible to obtain a valid bootstrap ...
1
vote
0answers
19 views

Randomization Testing help

I am scratching my head over this one...any help would be greatly appreciated. I want to know if the average travel time between Guell Park and the beach in Barcelona using the bus or the metro ...
1
vote
0answers
53 views

Structural equation model: Bootstrap, Bayesian statistic or rescaling binary data to deal with small sample size?

I’m trying to solve a SEM. Mi model includes 4 latent variables and 11 observed variables. My data is binary. Question 1: I just have 47 observations and in the literature a sample size over 150-200 ...
3
votes
0answers
46 views

Asymmetric confidence intervals on bootstrap estimates

I've performed bootstrapping on my leastsq parameters and now I have a load of data from which I can get the mean and standard deviation for each parameter. Lovely. But when I look at a histogram of ...
2
votes
0answers
35 views

The Bayesian approach to computing estimator bias and variance

From what I understand, jackknife and bootstrapping are frequentist methods for computing statistics (bias, variance, etc.) of an estimator. Given a sample of my data and an estimator, and assuming ...
2
votes
0answers
40 views

Bias and variance estimation with boostrap

The Wikipedia article about Jacknife estimation of the bias and variance of an estimator $\theta$ includes the following formulas: Variance of $\theta$: $ \operatorname {Var}(\theta )=\sigma ...
2
votes
0answers
26 views

Comparing predictions from different mixed models

In my analysis I am running two versions of the same linear mixed model, the only difference is that in one model the response variable is observed and in the second the response variable is ...
1
vote
0answers
38 views

Creat a confidence interval for non normal data

I have a data set based on percent time a fleet of machines were available each month, as a result the majority of the observations are close 100 with a few being lower and one or two being 0 this ...
1
vote
2answers
94 views

Bootstrap to evaluate variance of AUC ROC

I have a toy dataset and want to eval the AUC - ROC with bootstrap ...
0
votes
1answer
61 views

Bootstrap aggregation (bagging) of logistic regression classifiers

So I'm taking N bootstrap samples and training N logistic regression classifiers on these samples. Each classifier gives me some probability of being in a binary class and then I average these N ...
1
vote
2answers
50 views

Clustered data WITHOUT multilevel / GEE model?

I have a data-set with around 700 observations from 12 centres. Although the clustering effect as tested in a random intercept model didn't seem significant, it seems more appropriate to use a ...
8
votes
5answers
200 views

Rule of thumb for number of bootstrap samples

I wonder if someone knows any general rules of thumb regarding the number of bootstrap samples one should use, based on characteristics of the data (number of observations, etc.) and/or the variables ...
1
vote
1answer
103 views

Bootstrapping approach: how is the resampled sample chosen?

Bootstrapping approach is based on the assumption that the sample is the population. Then, a resampling of the sample is done, replacing the individuals of the population by chance. The number of the ...
1
vote
0answers
40 views

Bootstrap confidence interval for a biased estimator

I have a statistic $\hat{\theta}$ for the parameter $\theta$. Which may be biased. Assume $\mathbb{E}[(\hat{\theta}-\theta)^2]=\textit{ecm}^2$ and $\mathbb{V}[\hat{\theta}]=\sigma^2$ are known, but ...
1
vote
1answer
61 views

Bootstrap with replacement with small number of repetitions

In this youtube video about bootstrap resampling, the creator states that when the number of bootstrap processes is small, the distribution of the parameter being estimated can no longer be thought to ...
2
votes
0answers
50 views

Comparing Gini coefficients: Variance estimation etc. needed?

In a project on software measurement, we plan to use aggregating statistics (e.g., Gini) to describe the concentration of certain observed program attributes (size) among program units (e.g., modules, ...