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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 ...
Marine Régis's user avatar
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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 ...
Sameer L's user avatar
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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 ...
Marine Régis's user avatar
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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 ...
Ryan's user avatar
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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,...
akoontz11's user avatar
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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) ...
OzkanGelincik's user avatar
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2 answers
124 views

Proof of property of Mallows/Wasserstein metric

Let $\mathcal F_p=\{H\text{ is a cumulative distribution function}:\int|x|^pdH<\infty\}$. Define on $\mathcal F_p,$ Mallows' metric ($p$ Wasserstein metric), $d_p,p\ge1$ for two random variables $X,...
reyna's user avatar
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Comparison of confidence intervals: bootstrap & exact resampling

Consider data $X_1,...X_n$ generated from a probability distribution $F$ with density $f$. I'm interested in constructing confidence intervals for a parameter say, $\theta(F)$. Via Monte Carlo ...
reyna's user avatar
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2 votes
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53 views

Multivariate meta-analysis with sparse multivariate array

I am wishing to conduct a meta-analysis that has many end-point measures that are all supposed to reflect a single concept - let's call it poor mental health. That would include things like ...
J Taylor's user avatar
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Resample from a sample to match a desired distribution

Suppose I have observations $x_1,\dots,x_n$, sampled iid from some distribution on $\mathbb{R}$, with pdf $p(x)$. Suppose I wish I had a sample from the distribution with pdf $q(x)$. Is there a way ...
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Formal testing for differences in kurtosis between two samples when bootstrapping suggests a difference

My question is similar to Testing difference in kurtosis between two samples where a comment suggested Unless you are looking for an enormous difference in kurtosis, it's unlikely any physically ...
StrongBad's user avatar
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How can Bootstrapping explain the uncertainty of a statistic?

I have been reading about bootstrapping, and sampling distributions, and find it odd that people use these techniques to describe uncertainty. As I understand it, the sampling distribution shows ...
Connor's user avatar
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Why does my bootstrap sampling distribution no resemble the true sampling distribution?

In trying to understand the bootstrap method, I have taken a sample of 11 observations from a Poisson distribution with a mean of two. I have obtained the following sample: Assuming I do not know the ...
hhh3's user avatar
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Estimating sample size from (intra/inter) subsample variance

If samples taken from the population vary from one another by a certain amount, while the differences between the types of sample in any sampling event vary only by a certain amount, what formula can ...
user84614's user avatar
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Bootstrap for Fleiss' K reliability

I have a dataset with 5 raters and 129 nominal observations. I want to calculate Fleiss'K for inter-rater reliability and I need to use bootstrap to calculate the Confidence Intervals. I am using R ...
FrAiello's user avatar
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2 answers
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Should we (under- or over-) sample when training a ML model, if we care about edge cases?

I know this question has been somehow reiterated in multiple ways, but I have not yet found an answer that would explicitly apply to my case. I wish to train a classification model to predict who is ...
BloodthirstyPlatypus's user avatar
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Tukey's HSD after permutation tests. Theoretically possible?

I'm interested in making more use of tidymodels' infer package, which lets one perform a variety of statistical tests by a simple algorithm. Here's an example pipeline for an F-test, where we aim to <...
h088bmIuXaskpzJEe3ld's user avatar
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Un-binning/Upsampling Ordinal Year-Bins into Individual Years for Random Forest Likert Analysis?

Question: Is it "quantitatively sound" to decompose/upsample year-bins (e.g., 2002-2006) into the component years when analyzing Likert Score data that was collected as a recollection of ...
stevelom's user avatar
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Resampling as prior distribution?

Suppose we've got a small dataset that we have no prior knowledge about and we're going to use linear regression on it. I have been wondering whether instead of fitting a normal OLS it would make ...
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Is it valid to incorporate the values I resampled from my binary dataset into a regression analysis?

I have binary dataset consisting of 15 zeros and 40 ones. To address potential bias in my data, I have calculated the probability of success from each cell by fitting a glm with binomial family as ...
Non_Praying_Mantis's user avatar
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Estimate overall descriptive statistics (such as mean, Std, CI ) for 100 resampling with same sample size and same population

I have a sample of data with n=200 and I would like to predict my descriptive statistics such as mean, Std Dev and 95% CI when I have specific sample size for example n=10 based on the current study. ...
Ati's user avatar
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4 votes
1 answer
180 views

bootstrap confidence interval and p-value calculations for finite population sizes

I am comparing the difference of medians between two groups of sample sizes $n1$ and $n2$. I would like to confirm that my boostrap approach for finite population size without pooling sample data ...
Docuemada's user avatar
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Do autoregressive coefficients obtained with the Durbin-Levinson algorithm, the Yule-Walker system of equations, and OLS coincide?

I am applying the sieve bootstrap for time series introduced by Kress (1988), which requires the estimation of autoregressive models with the Durbin-Levinson algorithm (generalized to the multivariate ...
Mr Frog's user avatar
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1 answer
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Is the following algorithm for bootstrapping test valid?

I have the data (samples) that represent population sizes of different bacterial cultures from 4 distributions for which I would like to compare the ratio of the means between them. Specifically: $...
Treex's user avatar
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2 votes
1 answer
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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 ...
GaryTheBaddy's user avatar
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227 views

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 ...
panda's user avatar
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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 ...
JanR's user avatar
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2 votes
1 answer
395 views

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 ...
stat_is_quo's user avatar
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1 answer
50 views

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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1 vote
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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 ...
EngrStudent's user avatar
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2 votes
1 answer
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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 ...
Lost1's user avatar
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1 vote
1 answer
188 views

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 ...
ReiMon's user avatar
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2 votes
0 answers
45 views

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 ...
Luc's user avatar
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1 vote
0 answers
73 views

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 ...
dotsbyname's user avatar
3 votes
1 answer
109 views

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: ...
Fanfoué's user avatar
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1 answer
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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 ...
Ggjj11's user avatar
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1 vote
1 answer
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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 ...
0 votes
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161 views

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 ...
infinite789's user avatar
1 vote
1 answer
48 views

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 ...
flyingmonkey's user avatar
1 vote
1 answer
23 views

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, ...
anpami's user avatar
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2 votes
0 answers
290 views

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 ...
mrpeverill's user avatar
0 votes
1 answer
42 views

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 ...
Jose Manuel Albornoz's user avatar
2 votes
2 answers
528 views

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 ...
qoheleth's user avatar
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1 vote
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101 views

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/...
Mirko's user avatar
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621 views

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 ...
3487564's user avatar
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1 vote
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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 ...
user358084's user avatar
1 vote
0 answers
44 views

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, ...
Vincent Granville's user avatar
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1 answer
179 views

"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 ...
arilwan's user avatar
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1 vote
1 answer
215 views

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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