Questions tagged [bootstrap]

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

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19 views

Hypothesis testing for difference in medians vs. median difference

I found this post saying that one should test for the median difference instead of the difference in medians, in particular if the data is skewed: http://onbiostatistics.blogspot.com/2015/12/median-of-...
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Bootstrapping the t-Test

my problem is of practical nature regarding bootstrapping the t-Test. My approach is to code a function which resamples the data vector and calculates the t-statistic for each new (resampled) vector. ...
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Bootstrapping in logistic regression with sparse binary variable

I would like to estimate the probability of a relation between two entities. The data set includes information of many relations between many entities, and information on covariates for each entity. ...
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How can bootstrapping help in meeting assumptions of linear regression

I was reading through my stat book and it was written that bootstrapping can relax the distribution assumptions for linear regression generalizability. I do not quite understand what assumptions we ...
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What is the point of using bootstrapping in logistic regression of sentiment analysis?

Apologies if the title is a bit ambiguous. Let's say I am trying to use logistic regression to predict whether a book review is positive or negative using the bag of words approach. My homework asks ...
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Safety stock calculations using Intermittent Bootstrap

Together with fellow students, I’m working on an assignment to calculate safety stock levels in case of intermittent demand. We’re already able to simulate the demand like in the paper of Willemain, ...
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Using Bootstrap for an arbitrary statistics

I know that we can use bootstrap to get confident intervals for mean and quantiles (like medians). However I have 2 questions: 1/ The confident interval for median we get from bootstrap, can we ...
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Good proportion of training data to get prediction interval using bootstrap

I am trying to get prediction intervals thanks to bootstrap: I train 1000 linear regressions with different subsets of my training data. Say I have 1,000,000 rows in my dataset, what would be a good ...
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Implications of Different Bootstrap Procedures for Estimating Difference in Means

I'm having trouble understanding the difference between two bootstrap procedures to evaluate the difference in means between two samples. As an example, consider the following scenario: My goal is to ...
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Running a simulation: replications or bootstrap?

I draw $n$ iid random draws from a univariate Gaussian distribution. My goal is to estimate the density $f$ using a nonparametric density estimation approach. To add a measure of uncertainty, I want ...
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Bootstrapped distribution of RMSE

I have two distributions of volume conservation factors (VCF) Generic and Generic Masked that I want to compare. The VCF being optimal if equal to 1, I want to show that one distribution is ...
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Bootstrapping Confidence intervals for model coefficients in R [closed]

I'm trying to create some confidence intervals for my regression coefficients via the non-parametric bootstrapping method. however, as my code currently stands, I'm only receiving a bootstrapped CI ...
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Model Comparison Tests with Bootstrapped Models

I've found myself in a catch-22 of wanting to conduct model comparison tests (like the Likelihood Ratio Test) but having to accommodate non-normal data. I know the LR test is invalid with Robust ...
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Hypothesis testing with composite populations of unequal size and variance

I am trying to test whether two populations have different means. Let's call the populations "Glaciated" and "Unglaciated." Each population comprises data collected at a number of rivers (9 for ...
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Multiple R-squared from bootstrap in R

I am fairly new to R and am having issues with my bootstrapped linear model. I'm using non-parametric case re-sampling to account for some skewed variables. Here is what I have done so far: ...
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Are positively biased bootstrap-derived GAM predictions indicative of model issues?

all, I am using a negative binomial GAM fitted with mgcv::gam to estimate counts for new data, and I wanted to use bootstrapping to find a 95% confidence interval for point estimates. In my ...
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Choice of statistic for Bootstrap confidence intervals

I am learning about the bootstrap (from this book) and have some questions about best practices. The usual approach seems to be to draw bootstrap samples of the same size as the observed sample. ...
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confidence interval for mean based on small sample when CLT does not hold

I have looked at similar questions but could not find an satisfactory answer. Please forgive if I'm wrong. I have a small sample (n = 24) and use the sample mean as estimator of the true mean. I want ...
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Completely asymmetrical confidence intervals (Bootstrapping)

I have calculated a Kendall's tau-b partial correlation coefficient (N > 2000). I have calculated a bootstrap confidence interval around the estimate, but what I find is that the confidence interval ...
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1answer
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Is this an appropriate use of bootstrapping? variability in a fleet's fuel economy

I have a data set consisting of the make and models of a diverse fleet of vehicles (e.g. 100 Honda Civics, 200 Ford F150s, 10 Tesla Model 3s). My goal is to estimate the average fuel economy of the ...
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Confidence interval of bootstrapped difference of means

I am attempting to calculate a confidence interval for a difference in 2 populations by means of bootstrapping in order to compare this to an interval calculated via normal theory. I am getting stuck ...
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Nonparametric bootstrap: Circular reasoning behind colleague's comments?

I have developed an iterative stochastic optimization search procedure that improves on a single initial guess until some desired threshold is reached, similar to how simulated annealing proceeds ...
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Bootstrap test for comparing two variances

How to use bootstrap to test the equality of two variances?
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Probability of a value given a dataset

I am trying to figure out a way to determine how extreme a particular value is from single variable, when the distribution of the variable is unknown. I have tried doing this with a kind of boot-...
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Robust regression vs bootstrapping of confidence intervals

In multiple regression, when the independent variable is not normally distributed and the dependent variable is not normally distributed, is bootstrapping of the confidence intervals or robust ...
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Parametric bootstrap: How many simulation?

Consider a parametric model $(P_\theta)_{\theta \in \Theta}$ on a sample space $(\mathcal{X},\mathcal{B}).$ Assume that we have an estimator $\hat \theta$ for $\theta$ but we are interested in $f(P_\...
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Why is the error rate from bagging trees much higher than that from a single tree?

I'm running the classification method Bagging Tree (Bootstrap Aggregation) and compare the misclassification error rate with one from one single tree. We expect that the result from bagging tree is ...
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What is the meaning of component err.rate of class randomForest?

I asked this question on Stackoverflow, but it's likely that I will received no answer on that site. So I cross-post my question here. I'm using the function ...
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Should the model be trained in every Bootstrap iteration?

I am working in a classification scenario and I want to calculate a 95% confidence interval for my test set error rate. When I am using the Bootstrapping method: Shall I train the model once on the ...
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How to test whether the simulated mean differs from observed mean?

I have three log-return time series data1, the data look like: ...
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Calculate bootstrapped confidence intervals for c-statistics in a logistic regression using DescTools::Cstat

A reviewer requested that we provide uncertainty measure of our c-statistics, and I guess 95% confidence interval is a good answer. I checked the manual of ...
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How do you calculate a paired risk ratio and its confidence interval?

I have some data showing the number of times individuals make GP appointments at the week and at the weekend, over a period of about 5 years. The data also show the number of times each individual ...
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Does a clustered bootstrap procedure have assumption about independence of errors?

I am performing a mixed model analysis including a lagged outcome variable (cross-lagged panel analysis in R using package lme4) which violates the assumption of independent errors (since they are ...
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What distribution do statistics callculated from small samples follow, when drawn from a Gaussian parents distribtution?

My question arises because initially I wanted to make a short study comparing the Median Absolute Deviation $\rm{MAD}$ as an approximation of the standard deviation, $\sigma$. The $\rm{MAD}$ is a ...
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disadvantage of bootstrap (from wiki)

In wikipedia about disadvantage of bootstrap it says: The apparent simplicity may conceal the fact that important assumptions are being made when undertaking the bootstrap analysis (e.g. ...
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How to tell whether one regression model is significantly better than another? Bootstrapping? CV?

As part of my PhD research I'm trying to find a good predictor of $y$ using $x1$, $x2$ and $x3$. (Out of interest, I'm in psychoacoustics. $y$ is the strength of a perceptual attribute elicited by ...
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Bootstrapped interpolated values from a model in glmmTMB?

Is there a way to bootstrap interpolated values from a model in glmmTMB? After I have fit a GLMM I like to interpolate known response values into the model using a modified version of Venables' ...
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Is it necessary to simulate unbiased coin in using frequentist approach for determining if coin is unbiased?

I’m trying to determine the best way to detect if a coin is unbiased, given some desired alpha. I understand basic probability/statistical inferencing, but there’s some information out there that ...
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When can you apply the bootstrap to time series models?

Under what circumstances can you apply re-sampling techniques to quantify the uncertainty about the parameters of a time series model? Say that I have a model such as below: $ Y_t = X_t\beta + e_t$...
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Creating standard deviation for ratio of two medians

I'm working on a project and am creating an index based on two medians (median cost/median income), with a small number of observations (<100). I'm trying to figure out how to properly calculate ...
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1answer
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Resampling procedure for determination of sample size

I am currently reading this article. In page 4, a resampling procedure is detailed: To explore the effects of sample size on estimates of population mean and standard deviation, we sampled 42 ...
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estimation of polynomial regression: bootstrap approach

Assume, one deals with polynomial regression, i.e. $$ y_{i} = \beta_{0} + \beta_{1}x_{i} + \beta_{2}x_{i}^{2}+ \dots + \beta_{m}x_{i}^{m} + \varepsilon_{i}, $$ where $i = 1, \dots, n$, with $m < n$...
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How to do a power calculation for a non parametric test?

I have a sample from a control population of smartphones, with their number of failures in the first year. I need to establish a rule to know if a new sample could come from the control population or ...
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Is there a bootstrap 're-sampling the residuals' equivalent for GLM?

In linear regression, I have read of a non-parametric bootstrap being done by 're-sampling the residuals (errors)'. The general idea being that you perturb the mean response by simulated values of the ...
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Reducing sample size for meaningful p-values

I'm analyzing correlations (using phi and chi-square) among pairs of items within a large dataset (1.2 million records). My understanding is that it's more likely to obtain spurious associations that ...
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1answer
133 views

Bayesian Bootstrap interpretation

I am using Bayesian Bootstrap for some analysis. Given dataset $X=\{x_1, \dots, x_N\}$, we generate bootstrapped samples $X_1,\dots, X_K$ by sampling from the $X$, with replacement. In classical ...
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1answer
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Why ever use a quasipoisson model instead of bootstrapped poisson GLM?

A poisson GLM and a quasipoisson regression model will given identical point estimates for the beta parameter of the linear predictor. The quasipoisson model is typically used when there is ...
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Efron bootstrapped confidence intervals does not include corrected value of metric

So in order to correct for optimism on my model I have used the Efron bootstrapping to calculate the optimism to correct my apparent value of several validation metrics for my model (repeated 500 ...
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Confidence intervals from bootstrapping in logistic regression

If I am using Efron bootstrapping in order to get the optimism of several metrics of my model, I need to calculate a new model on a bootstrapped sample, then use it on the bootstrapped sample AND on ...
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Bootstrapping logistic regression coeffs with z-scored predictors in logistic regression

Hoping someone can help clarify why there is a discrepancy in regression coefficients when applying bootstrapping to z-scored vs. raw data. There is probably a simple explanation but in all my ...

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