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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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block bootstrap implementation for `gls` from `nlme`

I have several datasets of timeseries data (days) with an experimental intervention on some days, where some of the datasets cover multiple sites. I also have matching day-level and site-level ...
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Pooling Participant Data to Resolve Limited Trials per Condition Issues

Problem: In an experimental task, there are 4 conditions with only 2 trials each, resulting in 8 observations per participant. Typically, we use Signal Detection Theory (SDT) to calculate descriptive ...
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Sandwich variance estimator or bootstrap-based variance for stabilized inverse probability weighting (IPW)

Multiple published papers describe IPW as akin to having population with multiply copies of the same individuals. Hence, the correlation should be accounted and corrected using sandwich variance ...
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Computing p-value using bootstrap bias-corrected and accelerated (BCa) confidence intervals - R

The boot.pval package throws an error when computing BCa p values. This suggested fix seems to work but I am unsure if this correct. The fix was suggested more than two years ago without having being ...
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Issue with bootstrap confidence and prediction intervals of mixed effects model predictions [migrated]

Recently I have asked a question on how to generate meaningful bootstrap confidence and prediction intervals for mixed effect models predictions in R using bootMer ...
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Bootstrap sampling to get monthly statistic from daily data

I have daily (iid) data for historic winter seasons: $d:$ (price, value, temperature, etc). The "value" is actually a concave up function of "price" and the other covariates. I'm ...
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Looped Regression and Inference in Stata or R

I have a sample of panel data and am running regressions on a loop for values of a specific categorical variable. For illustration: $$ y_{it} = \alpha_i + \eta_t + \beta^C D_{it} + \epsilon_{it} $$ ...
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Is it possible for some p-values to be impossible? (because statistic generated by parametric bootstrap is mostly the same value.)

I am using a parametric bootstrap/monte carlo hypothesis testing method to generate the null distribution of the log likelihood ratio statistic. However, I am worried I might be doing it wrong ...
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Why are confidence intervals valid? [duplicate]

Recently, I began my statistics journey to understand the field better. Previously, my experience with statistics consisted of memorizing formulas, conditions, and applications of the latter. While ...
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How to actually use the Empirical Influence Function for BCa Bootstrap Intervals?

In the course of seeking reassurance for another part of a hobby analysis* I found a stack answer which mentioned The Jackknife, the Bootstrap and Other Resampling Plans (Efron, 1980). Having managed ...
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How to express the uncertainty in the OR of a right-tailed Fisher's exact test?

Many OR (odds ratio) visualizations show 95% confidence intervals expressing uncertainty like on the left panel in figure below. However, in genomic overrepresentation analysis right tailed Fisher ...
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"Important" data points causing problems with nonlinear regression bootstrapping

I am trying to model radar backscatter of a planetary surface. The power which is scattered back to the instrument depends on the angle at which the it observes the surface. The shape of the resulting ...
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Bootstrap confidence and prediction intervals of mixed effect model predictions

Let's say I fitted a mixed effect model mem with the lme4 R library, and I would like to use the ...
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Query about one step in AR-sieve bootstrap

I am trying to understand how an AR-sieve bootstrap works. I am reading this paper: Here are the steps which are to be taken to do a sieve bootstrap:- We are given a sequence $ X_1,..., X_n $. We ...
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What makes a statistic valid for monte carlo simulation?

A while back I was reading Garland et. al. (1993) about studying whether two groups of animals, say herbivores and carnivores differ in their mean value for some trait, like the amount of territory ...
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Calculate inter-rater noise using Kahnemans (2021) approach

I need help calculating signal and noise based on the method described by Kahneman et al. (2021) in their book "Noise." They provide a technique for quantifying noise between raters ...
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Is Bootstrapping Independent Time Series to Construct Prediction Intervals Valid?

Question: I have a dataset consisting of multiple univariate time series, each representing an independent sequence of insurance claim amounts over time. My goal is to predict future claim amounts ...
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Why is it recommended to keep use.u=T (in bootMer) when doing parametric bootstrap for lmer models?

I am performing a parametric bootstrap with the intention of using the simulated values to create confidence intervals for my coefficients in a mixed model. I saw that it was generally recommended to ...
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Bootstrap standard errors after multiple imputation

Following Rubin's rules for multiple imputation, I've calculated pooled estimates, group means in this case, with pooled standard errors. I checked this with a bootstrap and, assuming pooled standard ...
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Using bootstrap to estimate covariance of mean estimators from two distinct (dependant) populations

Given two samples : an i.i.d. $n_u$-sample $(u_{j})_{1 \leq j \leq n_u}$ and an i.i.d. $n_v$-sample $(v_{i})_{1 \leq i \leq n_v}$. Note : The populations of the two samples are disjoint (let's say we ...
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Standard error of mean with varying replicates for batches

I have three batches of simulations and I calculate a specific property from these (not important for the question). I want to calculate combined SEM for these three batches Batch | #samples | ...
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Cluster bootstrap for standard errors using fixest

I am trying to estimate a model with fixed effects using feols from the fixest package. As I only have few clusters, I would ...
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What is the advantage of using bootstrapping to estimate variance?

I'm working through an online course on Hypothesis Testing where a one-sample test of proportions is done using bootstrapping. I have some grounding in statistics, where the test statistic $ z $ for a ...
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What is the fundamental "problem" why bootstrap intervals tend to be too short?

I found several posts which perform simulations and demonstrate that bootstrap intervals tend to be too short (even when accounting for the correct dependency/grouping structure). This is repeatedly ...
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Bootstrapped confidence interval overestimates variance in difference of means

I'm trying to translate a Welch T-test into a bootstrap sampling scheme. However, I'm finding that the bootstrapped 95% confidence interval is much larger than the Welch T-test. Perhaps this is just a ...
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Standard error in two-sample T-test

Reviewing T-test basics and from a book (Hypothesis Testing, Jim Frost) the following plot is displayed. ...
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Bootstrapping Methods for Logistic Regression Models

I have a continuous numeric predictor variable and a continuous numeric response variable (the response variable is bounded by 0 and 1, and there are 0s and 1s in the response column, but the ...
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Should I use parameter standard deviations or standard errors when bootstrapping to generate confidence intervals about logistic regression models?

I have binary response data that I've modeled using simple logistic regression. I have data from a few species and from several individuals of each species. I've modeled each individual separately, so ...
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How can I provide meaningful commentary about the uncertainty associated with a population estimate drawn form individual ML predictions?

Context: Suppose a team develops a prediction model that predicts the presence of a condition for a given individual. This model is trained and externally validated before being picked up by a ...
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Is bootstrapping inherently Frequentist? If so, how do we do a Bayesian non-parametric two-sample test?

I normally use frequentist statistics but I now want to use Bayesian statistics as I want to carry out a two-sample (randomised control trial) test that includes prior information. I have an existing ...
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How to interpret the result of a bootstrapped multiple regression?

My data didn't have normally distributed residuals, so I have used bootstrapping to run a multiple regression analysis. My model is the following: independent variables A and B predict the dependent ...
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When can I use bootstrapping in a microsimulation model?

I'm investigating uncertainty in a linked series of microsimulation models, the outputs of each feeding into the next. The first model (model A) generates individuals by resampling them - using a ...
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Moving block bootstrap to get CI around first derivative of trend

I have a time series and I want to know the rate of change of the nonlinear trend component. To do this I want to take the first derivative of the trend but I need a confidence interval around it. I ...
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Evaluation of BaggingClassifier using Out-of-bag observations is an indirect evaluation of bagging classifier?

May I know is the following correct? When evaluating a BaggingClassifier using out-of-bag (OOB) samples, the goal is indeed to assess the performance of the ensemble as a whole, rather than focusing ...
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How does whites bootstrap reality check work? [closed]

I am struggling to understand how it works regarding technical analysis and predicting a superior trading rule.
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Estimating Confidence in Feature Rankings from Multiple Experiments with Non-Normal Data

Hello dear Cross Validated Community, I am a new doctoral student in bioinformatics, and I am working on a project involving multiple experiments, each generating a single numerical result for each of ...
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Determine a robust trend from noisy time series data, when start and end years have a material effect

I have about 20 years of data, each year has a number of observations. If I put a linear trend through the data, I get a trend, and this trend differs based on the the choice of start and end year, ...
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Calculating true/population standard deviation from bootstrap standard deviation/standard error

I am using the coffee_ratings dataset to do a proof-of-concept calculation to estimate the population (i.e., coffee_ratings) ...
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Compare bootstrap auc confidence interval using t-test

In order to choose between a machine learning model when the number of features is 5 and a machine learning model when the number of features is 6, I want to bootstrap the auc of the model to obtain a ...
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Select classification model using nested cv and bootstrap auc confidence interval

My goal is to find the best 1 model out of 55 classification models. I first ran nested cv on 55 models to see which model had better generalization. The AUC score was used as an evaluation indicator. ...
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Bootstrap on samples with different precision

Imagine the following setup: I have N specimens that come from the same population (e.g., they are from the dame material) For each specimen i, I have a single estimate of a given property in terms ...
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Number of distinct bootstrap (re)samples with restriction

What is the number of distinct bootstrap (re)samples, when a specific element cannot appear more than 3 times in the resample ( or k times in general) ? when k=n then we got the general solution: ${2n-...
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ACME Significant in Mediation analysis, but not Proportion Mediated and Fitting terminated with step failure warning

I am running a series of mediation analyses in R using the mediation package and the following code: ...
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Bootstrapping survival data to obtain confidence interval for a function of median survival time

I have a group of survival data, mature enough so that its sample median can be estimated by Kaplan-Meier method. I'm hoping to estimate a quantity which is an increasing function of the median, and ...
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Bootstrap method with 2 Fisher matrices in order to do the cross-correlations between both

I have 2 Fisher matrices where each colum/row represents the information (in Fisher's sense) of astrophysical parameters. These parameters are in the same order for both matrices. Now, I would like to ...
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Validity of bootstrapping for estimation of annual maxima distribution

I am working with a large timeseries (millions data points) spread across 5 years from which I would like to estimate the annual maxima distribution and subsequently a quantile of this distribution. ...
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Point-level prediction intervals in LightGBM models

I would like to compute prediction intervals for LightGBM at the sample level. In other words, given a certain row to be classified (supervised classification, not regression), what is the upper bound ...
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SPSS computation of the 2-sided bootstrap p-value

In the SPSS Statistics Algorithm Manual Version 29 (p.132 in the pdf) the computation of the two-sided p-value for the bootstrap procedure is described as follows: Sadly, because of different RNGs, I ...
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Block length in time series bootstrap of AR(1) model, biased AR coefficient

I'm using block bootstrapping for some simple autoregressive time series models, and I'm running into pretty high bias in the bootstrapped estimates of the autoregressive coefficients, even from large ...
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Negative optimism resulting in better adjusted model performance

After carrying out 200 bootstraps to estimate the optimism around the net benefit of a model developed using stepwise BIC logistic regression, I'm getting a negative value meaning that when subtracted ...
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