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Questions tagged [bootstrap]

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

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Efrons enhanced bootstrap - estimate calibration equation parameters

"The bootstrap method is as follows. From the original X and Y in the sample of size n, draw a sample with replacement also of size n. Derive a model in the bootstrap sample and apply it without ...
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Simulated estimate from a linear mixed model with random intercepts

I wanted to see if there was a quick way to get a bootstrapped parameter estimate and CI using confint() from merTools? ...
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Error in Bootstrap estimates [on hold]

From Efrons "An Introduction to the Bootstrap", It says that typically Bootstrap estimates are nearly unbiased. I'm looking for a rigorous proof to demonstrate this. I have tried to use conditional ...
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Can we use bootstrap in time series case?

I use random forest for time series forecasting.I have some features: lags. day of year,day of week,hours,minutes. ...
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For what reasons should one use Bootstrap Testing when the sample size is large?

Bootstrap testing is great if we have a small sample size where the Central Limit Theorem does not apply. However, what are convincing reasons we should use Bootstrap if we do have a large sample size....
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Bootstrap Methods - Mathematical Rigour

I'm looking for sources that present an Introduction to Bootstrap Methods in a mathematically rigorous way. I have looked at books such as "An Introduction to the Bootstrap - Efron" and "Bootstrap ...
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29 views

arbitrariness in bootstrap bias estimation

The bootstrap estimates bias by applying the "plug-in" principle to $$E(\hat{\theta}_n) - \theta$$ I got this knowledge from p.124 of Efron, Tibshirani, 1994. equation(10.1) $\text{bias}_F=E_F[s(\...
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CI using Bootstrapping on GLM (log link, gamma dist)?

I am running this exercise on SAS but don't think the choice of software is relevant: the question still legit when I am using R or others. I was working on medical claims data (typically having ...
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Model comparison when using Harrell's bootstrap-based optimism-correction Internal Validation procedure

I have 2 non-nested models that I would like to compare with respect to their predictive accuracy (concordance index). I have used Harrell's (Efron-Gong) bootstrap-based optimism-correction internal ...
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How to compare model performance across subpopulations?

I'm trying to determine if a model I'm working with performs differently across subpopulations of the general population I'm training on. Let's say that my population $P$ is made up of three ...
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Likelihood-Based Confidence Intervals for ratio of risk ratios

Ratio of Risk Ratio (RRR), along with Relative Excess Risk Due to Interaction (RERI), has been used to quantify the joint effects of 2 exposures in epidemiology. Quoting Joshua N. Pritikin CIs ...
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Resampling-based Confidence Intervals for Relative Excess Risk Due to Interaction

Relative Excess Risk Due to Interaction (RERI) has been used to quantify the joint effects of 2 exposures in epidemiology. RERI is the proportion of disease among those with both exposures that is ...
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Is bootstrapping suitable for deriving prediction intervals in models which randomly sample from distributions?

I'm working with a fairly complex predictive model which essentially produces total populations for different groups in future years. Joiners, leavers, and transitions between the groups are modeled ...
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Adjustment for binary classification with differing proportions

My data had a different proportion of 1 (20%) and 0 (80%). I found here that we can use upsampling to get a good sensitivity. The caret package in ...
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14 views

Confidence interval with an unknown constant bias

Assume that we have an estimator $T_n$ of the parameter $\theta$ where $n$ is the sample size and there exists an unknown constant $C$ such that $\sqrt{n}(T_n-\theta) - C \overset{d}{\longrightarrow} ...
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How to bootstrap data with unknown correlation structure?

I am interested in how to validly bootstrap data with an unknown correlation structure. Let's say I am bootstrapping in order to obtain inference for some smooth function of the data similar to a ...
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How to determine block size for a block bootstrap and it's variants?

I am working with time series data and wish to use bootstrapping to compute confidence intervals of the mean prediction of my model's accuracy. My data is not i.i.d and therefore I need to use a ...
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Bootstrapping confidence interval for regression prediction

I want to use the regularized regression technique such as partial least squares (PLS), Lasso, and the elastic-net. However, as far as I know, there is no "standard" way of calculating the prediction ...
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clustered multi-reader multi-case/modality ROC analysis in R

My problem I am looking for an R approach to: do a ROC-Analysis of (clustered (i.e. several observations per case, e.g. prostate segments) / multi reader / multi case/motality) rating (score 1-5) ...
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Is bootstrapping a viable method for augmenting time series data?

I have recently learnt about the bootstrapping method and I am using it in my model tuning phase of my current project. I am working with time series data and therefore have decided to use a ...
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Can cross-validation+bootstrapping be used to estimate the required sample size to achieve a “narrow” estimate of prediction accuracy

I am aware that the question may not seem very clear at first sight. So let me elaborate on this. I have certain data (let's call it "exploratory dataset") on which I have performed survival analysis ...
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Is it valid to use bootstrapping to generate a confidence interval of an accuracy measured by rolling window analysis?

I would like to generate confidence intervals for the accuracy of a model that I use to forecast time series data. In order to get an average accuracy for my model I use rolling window analysis (See ...
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Why does non-parametric bootstrap not return the same sample over and over again?

Why does non-parametric bootstrap not return the same sample over and over again? My notes write: Assume data $X_1,...,X_n$. Sample data with replacement to produce $X_1^{(p)},...,X_n^{(p)}$ Now ...
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Can I use bootstrapping technique to get a 95% conf. interval of conversion rate

I have a sample data about user conversion at an ecommerce site. The data contains the following variables: User Id Site visits - count of the number of times the user visited the site during the ...
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Almost Sure Convergence and Subsamples

My actual question is in the last paragraph, but I will start with a basic example. In the book "A Course in Large Sample Theory" (Ferguson), they present the Strong Law of Large Numbers as the ...
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How to correctly compare the accuracy of different forecasting methods using bootstrapping with time series forecasting

I am currently working on a forecasting project and I have tried several different models to forecast with. Having trained and tuned my models I want to pick which model works best for each time ...
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Is ordinary optimism bootstrap an updated version of 0.632 bootstrap?

0.632 bootstrap is an updated version of leave-one-out bootstrap which would overestimate the prediction error. The idea of "ordinary optimum bootstrap" comes from the idea "The sample to the ...
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Resampling for estimating the mean value

I am comparing plant growth results obtained with 6 different models. I do have results for 45 sites at 5 points in time and am ranking the models for each site and time. Now I would like to ...
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“Honest” Random Forest - one time subsampling + bootstrapping vs. repeated subsampling

I am wondering about the differences between the following two approaches when predicting with random forest: Approach 1: Divide your dataset into a training sample and a test sample (randomly) ...
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Recommended significance test for coefficients in regression mixture with bootstrap Standard errors?

I have fit a model that comprises of a mixture of regressions. Within each regression I have an estimate of the coefficient of each covariate within that component regression. The model was fit using ...
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How would you do a two-one-sided test with resampling (bootstrap)?

Say I have two samples and I want to test whether their mean difference is likely to be in the interval [-0.1, 0.1] (this is, I want to both reject the hypothesis that it is smaller that -0.1 or ...
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Is delta method better than bootstrap to generate standard error for marginal effects?

I read here, here, here, here, and elsewhere that " Parametric bootstrap closely related to objective Bayes. (That’s why it’s a good importance sampling choice.) When it applies, parboot approach ...
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Bootstrap confidence interval for slope of regression line, in the presence of heteroskedasticity

Suppose I have a simple linear regression problem, and I use the bootstrap to obtain a 95% confidence interval for the slope of the regression line. (Maybe I will use this confidence interval to test ...
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Alternative test to paired t-test? [duplicate]

I need to compare data from paired groups, but my distributions are non-normal, and my sample size is small. Are there any tests I could apply? I've been looking into non-parametric alternatives like ...
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Proper way to address case when parameter doesn't appear in likelihood of your bootstrap resample?

I know bootstrapping is an asymptotic result (your sample needs to be large enough to look like the actual empirical distribution), but what do you do if when you construct the likelihood of the ...
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Comparing two independent groups that follow a power-law

I have two independent samples that each follows a power law distribution. I want to investigate whether the average of one of the samples is greater than the average of the other sample. Which ...
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Bootstrapping CI for lme and plotting mean fitted values with CI

I'm modeling an over parameterized linear mixed effect model and am struggling to create a simple plot to show a mean fitted line of my model with the associated 90% CI. I'm going to use the ...
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P-value from sum of bootstrap vs ttest are different [closed]

I am learning about bootstrapping and I am calculating the correlation between x and y, between two independent groups with each size n=8 and n=24, respectively. Their distributions are non-Gaussian ...
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Optimism bootstrap with non-linear models

I have come across an example in my research with heavily overfit non-linear probabilistic classifiers, where the optimism bootstrap appears to underestimate the optimism, even when using a proper ...
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Comparing linear regression coefficient between 2 continous variables of 2 groups in SPSS

I have 2 groups A (apple N=40) and B (Banana N=40). The variable fruit is categorical. For both A and B I have 2 continuous variables (how much sunlight they got, and their color intensity). Sunlight ...
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Gini above 1 when bootstrapping

Let's say I have a dataset (data), which contains the binary target variable class and the predictions (probabilities in [0,1]). ...
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Why bootstrapping?

I understood that bootstrapping is a technique used to estimate statistics of a population. In bootstrapping we take many samples of chosen size, estimate statistics and obtain the mean of these ...
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Is “bootstrapping” in statistics the same as “bootstrapping” in machine learning?

In this article on temporal difference learning (TDL), there is a link to bootstrapping. However, the bootstrapping article seems to focus on quite different things, and I find it hard to see the ...
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Bootstrap and distribution of the test statistic

One rule of thumb says that we should avoid using the bootstrap to construct a confidence interval for some test statistic, if (1) the test statistic is greatly affected by outliers or by rare ...
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Bootstrapping in sampling distribution [duplicate]

What is the fundamental importance of bootstrapping? To generate a sampling distribution, what are the purposes of taking just one sample and resampling from it multiple times; as opposed to taking ...
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perform what if scenario with predictive model (in R)

Let us say I have a model with good predictive performance. The model has a few parameters X1, X2, X3, X4 and predicts Y. I would like to perform some 'what if scenario simulations' using this model ...
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2answers
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Evaluate the performance of a model with bootstrap

This question is about the application of the bootstrap rule The population is to the sample as the sample is to the bootstrap samples.I have a small dataset about lung cancer.There are 160 patients ...
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Inference with Generalized Additive Models: Bootstrapping and Posterior Simulation

It has been argued in the statistical literature (and by Simon Wood, the creator of the well-known and widely-employed 'mgcv' R package that bootstrapping does not perform very well for Generalized ...
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Explanation of confidence interval from R function boot.ci

I used boot function in R to do bootstrap for 40 times and used boot.ci to get the "normal" confidence interval. The following is my R code: 1. Define the statistic used in boot function ...