# Questions tagged [resampling]

Resampling is taking a sample from a sample. Common uses are jackknifing (taking a subsample, eg all values but 1) & bootstrapping (sampling w/ replacement). These techniques can provide a robust estimate of a sampling distribution when it would be difficult or impossible to derive analytically.

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### Extrapolating the total number of different molecules (equivalent of marble sampling)

We have a total material, 100%. We measured 2 samples of it, each 4.7%. We found 1538 different molecules in each sample, 1061 found in both, and 477, 477 found exclusively in either (but not both) ...
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### How to analyse serial brain sections probed for different proteins (ttests and potenial issue of independance)

Generic scenario: Brains have been collected from two different populations. They then have been cut into thin sections (serially) for the purpose of looking at the expression of proteins in specific ...
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### Distribution-free prediction intervals in linear regression

I've found some literature on the subject, but it is rather difficult to read. I am wondering if the following simplified method makes sense. My question is what part is correct in this methodology, ...
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### "SMOTE makes the assumption that the instance between a positive class instance and its nearest neighbors is also positive"

I am trying to get my head around this assertion by Liu, Y. et al (2011 pp. 7) about SMOTE oversampling technique that: because SMOTE makes the assumption that the instance between a positive class ...
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### How to resample members from the population who didn't respond after the survey?

I have used stratified random sampling on population to generate the sample. Now the issue is if after the survey is conducted some of the members in the sample ...
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### Highest amount of variation with resampling

I am looking for the right direction to find methods to solve the following case: Let's say that I have a sample of 1000 people which represents a real-world population. I am creating clusters/groups ...
1 vote
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### How does Michaud Resampling improve Mean-Variance Optimization?

Michaud Resampling claims to reduce estimation error through the following process: Step 1. Sample a mean vector and covariance matrix of returns from distribution of both centered at the original (...
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### Using the bootstrap for estimating the odds ratio standard error

I have the following table $$\begin{matrix} &R = 0 & R = 1 \\ E = 0 &n_{00} &n_{01}\\ E = 1 &n_{10} &n_{11} \end{matrix}$$ If needed, this is census data from a metropolitan ...
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### How to sample $n$ observations from a multinomial distribution using binomial (or poisson) sampling?

Context I have $n$ observations which I'd like to sample with replacement for the purpose of bootstrap. A way to think about it is that we have a multinomial distribution with $n$ classes and that we'...
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### Can I use a permutation test to test the null hypothesis ''The difference between two groups is X''?

From what I read on permutation test, the null hypothesis is usually that there is no difference between the two groups. I want to test if the difference between the mean of the two groups is $\theta$ ...
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### statistical tests for multi-armed bandit strategies

Assuming that I have 2 strategies (X and Y) for a contextual multi-armed bandit problem. I want to perform a statistical test for determining which of the two strategies yields the highest reward. ...
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### Resampling classes across weighted source distributions

I am sure this is a common problem, but googling only yielded false positives. I probably did not know what terms to search for. So here we go: I have $n$ classes from $m$ different sources. Each ...
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### Define sampling rate in the context of SMOTE

In this paper, authors claimed that the traditional SMOTE uses the same sampling rate for all instances of the minority class whereas their proposed genetic algorithm-based SMOTE (GASMOTE) algorithm ...
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### What statistical tests use resampling?

I have heard, that resampling techniques like bootstrapping are part of how many non-parametric statistical tests were developed/derived, but after going through the maths behind a few I have seen ...
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### Why does the best model in the training set have the worst test result?

I have trained eight models using 10-fold cross-validation, and evaluated the models by using resampling technique as described here. The result shows that SVM with sigmoid kernel (SVM-s) and random ...
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### Bootstrapped mean always almost identical to sample mean?

I'm running a simple bootstrapping experiment with the following code ...
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### K-fold Cross Validation for ridge regression model evaluation with specific lambda value in R

I have identified the optimal lambda for a ridge regression model using k-fold cross validation. However now I want to use k-fold cross validation to evaluate the model performance on different ...
1 vote
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### Is my understanding of Random Forest algorithm with bootstrapping correct?

I want to know whether my understanding of RF algorithm is correct when using bootstrapping. So, let's say I have a dataset of 100 observations. That dataset is then split into a 75/25 split of train ...
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### What's the meaning of using Bootstrap? Why should resampling from sample set have any difference? [duplicate]

I just learned Bootstrap Method from my Statistics course. The teacher says that the whole population is unknown, however we have some sample set $\mathcal{D}$ with sample size $N$. Then we use this ...
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### Bootstrap to control for confounding variable - sample size

I am interested in the effect of dichotomous variable A on several scores for a sample size of N = 469 (observational data). Most scores have a non-normal, asymetrical distribution. Here's the ...
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### How to get estimates for a population parameter when resampling distribution is skewed?

Let's say I want to estimate some parameter $p$ of a population. I have drawn a random sample $S$ from it. One way of doing so is to resample $N$ samples $S_1,S_2,\ldots,S_N$ using, say, bootstrap or ...
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### Why can't we sample without replacement for each tree in a random forest if the subsample size is large enough?

Usually if we have $n$ observations, for each tree with form a bootstrapped subsample of size $n$ with replacement. On googling it one common explanation I've seen is that with replacement sampling is ...
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### Is balancing class data for imbalanced problems helpful or just folklore when considering thresholds?

(In the context of predictive models) Caveat: I'm aware that imbalanced data questions are a dead horse, but I haven't found an answer to this flavor of it directly. When working with highly ...
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### Modeltime Resampling Panel Date - Vizualization

I'm using the modeltime Panel tutorial "Resampling Panel Data"from here: https://business-science.github.io/modeltime.resample/articles/panel-data.html. While accuracy is in the end ...
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### An alternative sampling without replacement

Consider a set $X := \{x_1, \ldots, x_n\}$ with corresponding weights $p_1, \ldots, p_n$. Suppose we would like to draw $m < n$ distinct (i.e. unique) elements in a way that the probability of ...
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### Accounting for repeated measures by resampling data and averaging parameter estimates misses the mark, but why?

Let's say I am looking at how unicorn herd size changes with food quantity. Unicorn herd sizes were surveyed at seven localities over the course of twelve months. Food quantity was assessed monthly, ...
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### Sample size is $10^7$, what happens if we bootstrap with replacement using subsample of size $5000$?

Let $\{X_1,X_2,\dots,X_n\}$ be a sample of $n$ iid observations of a random variable $X$, and let $\overline X_n = \frac{1}{n} \sum_{i=1}^n X_i$ be the sample mean. Now suppose we want to use ...
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### Ratio of two weighted sum of Bernoulli random variables

Given a vector $X=(x_1,\dots x_N) \in R_+^N$ and $N$ i.i.d Bernoulli random variable $A_i$ with $$P(A_i= 1)=p = 1 -P(A_i = 0)$$ First we define the random variable $T$ as  T = \frac{\sum_{i}x_i * ...
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### resampling of imbalanced dataset with only binary predictors and target

I am trying to classify an indicator of health as 0 and 1. I have an imbalanced dataset (0 : 5700, 1:1700) where all the values are binary (0 and 1 only for all features and target). I applied many ...
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