# 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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### Environmental filtering versus spatial resampling in species distribution modeling

I am building species distribution models using machine learning models based on GBIF data (presence-only data) and working on a very large spatial scale, encompassing all of North America. Before ...
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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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### should the standardisation of numerical variables be carried out before or after the rebalancing technique of the target variable?

I am dealing with a classification task of a binary target variable (company failure prediction yes or no) for a university project. I was wondering, should the standardisation of numerical variables ...
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### Choosing spatial resampling functions for species distribution modeling study with different pseudo-absence sampling methods

I am conducting a species distribution modeling study using machine learning models. Since I only have presence data, I have employed various pseudo-absence sampling methods, including random ...
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### What is the distribution of sampled variance from a finite population?

Suppose there are certain sample mean x and sampled standard deviation sigma given N, then n number of data is sampled from this finite sample without replacement . The variance of the resampled ...
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### What model is appropriate for measuring the correlation between two proportions?

Explanation: I have a dataset (say n=100 samples) with two data types associated with each sample (genetic and ecological). I'm resampling the dataset such that I take a random draw of n samples (2, 3,...
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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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### What kind of null hypothesis can a permutation test consider?

From what I have seen, permutation tests are always used to compare the means (or median) of two different groups in the experiment. Even Wikipedia says A permutation test involves two or more ...
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### Mice imputation with a small number of missing values - test/train set may have no missing values

When performing resampling, I wish to perform the same imputation on the test set as I performed on the training set, which is accepted practice. So, when imputing with MICE, I generate a predictor ...
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### Bootstrapping & resampling under the null - how and why?

My question is firstly wondering about the correct procedure on how to bootstrap under the null, and secondly more or less re-asking the following question which has not received any answers: Why ...
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### How do I estimate effective sample size from a bootstrapped sample?

Before now, I have typically used Kish's effective sample size estimator to estimate the precision in survey data with sample weights. I understand bootstrapping is another approach for estimating ...
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### Synthetic vs Data augmentation for low dimensionality data

I have problems understanding data augmentation. I currently have low-dimension features, each data point only has 3 features. A total of 20k non-linear data with only 3 features. I have generated ...
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### partial set coverage sampling rule

Is there a general relationship that gives the total number of drawn samples (d) on average required to have at least percent (p) of distinct samples in a set size N when sampling randomly with ...
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### Resampling a timeseries

I have a list of stock returns (say computed from the historical data) and would like to resample the historical return distribution. Naively doing bootstrapping means the samples are iid. I'm ...
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### How to generate data or sample data from a summary tables?

I have a dataset like this: I only have summary tables of the "loneliness score" for overall, for each sex, each age group, and sex * age interactions. Besides this, I have 23 items that ...
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### How to calculate 2-D confidence intervals with the boot package in R? [closed]

I'm making a scatter plot of two statistics X(e) and Y(e) for various values of scalar parameter e. The sampling distribution of both X and Y is not normally distributed. Now I want to calculate a 2-D ...
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### Bootstrap/resampling and bayesian regression, solving dependency problems

Let's say we have two experiment replications R1 and R2, (with some modifications in R2), deployed for intersecting (but not fully the same, let's say >50% but <80%) populations of participants ...
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### What are the minimal sample size requirements for cross-validation or bootstrapping?

I hope it makes sense to even ask these questions, but I'm wondering how can I evaluate the "validation" procedures that my data allow me to perform (i.e. cross-validation or bootstrap: ...
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### How many independet test-train splits (with independent training) should I perform?

I recently read some literature by Bagnall et al https://arxiv.org/pdf/1602.01711.pdf "The Great Time Series Classification Bake Off". If I understand them correctly, they advise to perform ...
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### Resources to understand the bootstrap

I am looking for any recommendations on resources to learn more about bootstrapping from a theoretical and rigorous perspective in terms of bounds/guarantees/etc. Any books or papers would be very ...
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### Can balancing of the majority and minority classes (RUS/SMOTE/...) improve AUC of a binary classifier?

Given that AUC is a threshold independent measure, can undersampling or oversampling of the majority/minority class during training improve the performance of a binary classifier? In my experience ...
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### Re-sampling at defined percentage of the population

Background: I have data that contains the severity scores of the pathology findings of tissues from different subjects with 100% sampling of the organ (let's call this dataset population, which ...
1 vote
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### Sample size determination for finding the "top 100" largest X among multiple datasets

My data sources Source A gives me a ranking of the top 1.000 largest X (say, companies). Source B provides a similar ranking, comprising 7.000 X, with some differences to Source A (different values, ...
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### Train-Test Split with nested groups and multiple balancing factors

I have a large (~15,000) sample of data from individuals nested within families (with about half the data points sharing a family). I want to split the sample in to a training and test set so I can ...
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### In which specific situations is minority class oversampling useful? [duplicate]

I understand that, in the context of a binary classification problem, downsampling the majority class is a useful strategy to come up with a smaller, computationally friendly dataset. Using this ...
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### Sub-sampling a dataset to a different target distribution without replacement - bias correction?

Suppose i have dataset $X$ and $Y$, and i want to sub-sample from $X$ so that the resulting (sample) distribution is as close to that of $Y$ as possible. One thing i can do is subsample with ...
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### Approximately Unbiased P-value vs Bootstrap Probability: which one should i choose?

Some references first: How is approximately unbiased bootstrap better than a regular bootstrap with regards to hierarchical clustering? Suzuki et al. 2004 https://www.researchgate.net/publication/...
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### For small sample sizes, is jackknife superior at controlling Type-I error compared to bootstrap?

This question is motivated by the post here: Can bootstrap be seen as a "cure" for the small sample size? In the referenced post, we see that the bootstrap approach does not control type-1 ...
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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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