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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Bootstrapping with dependent data
I am trying to construct an example of the problems that arise when the Efron bootstrap is applied to dependent data. I have the following hypothetical time series data set: $\{100, 101, 102, 103, 104,...
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Bootstrap residual resampling in R
Since I am quite new to this topic I have a question regarding bootstrap techniques in R.
I should generate a 10,000 bootstrapped time series by drawing with replacement from the residuals.
This ...
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wild bootstrap of regression models
I am trying to show the differences between the residual, wild and pairs bootstrap on a regression model in R. I understand the differences in how they are calculated but I would like to know the main ...
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Sampling Puzzle
There is a bag with N = 50 balls. Among which M = 10 balls are red, and N-M = 40 balls are blue. Further, say the red balls are numbered among themselves from 1 to 10, and the blue balls are numbered ...
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Probability of failure of Uniform Sampling [duplicate]
Say I have a bag with 10 numbered balls, and I pick one ball at each time step and then put it back in the bag. Since each ball is equally likely, therefore the current situation represents a uniform ...
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Bootstrap to Statistically Compare Accuracy of Different Approaches
I am currently dealing with a multi-class classification problem. I have two different approaches (in terms of feature engineering) to this problem. Intuitively, the result is obvious. However, I want ...
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Algorithm for sampling fixed number of samples from a finite population
I'm looking for an algorithm that would do the following:
Imagine that you need to sample uniformly at random and without replacement $k$ elements from a pool of $n$ elements. The catch is that $n$ is ...
Tim♦
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Are you always supposed to evaluate the performance of regression models?
I'm a bit confused. I am doing an analysis where there are about 70 observations of my dependent variable. I'm planning to do a multiple linear regression or multivariate logistic regression to see ...
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Different ways of resampling in OLS bootstrap
I have a constrained OLS model of form:
$$ y = X\beta + \epsilon
$$
After fitting, it turns out that there is heteroskedasticity present in residuals. Let's say I want to estimate the parameter ...
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Sample Selection within motion planning data
The target of this study is to attempt to learn behavior of an unknown algorithm from raw data.
The environment in use is a 2D motion planning environment. We assume the algorithm behaves similarly to ...
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Constructing a population random sample from subpopulations random samples of equal size
I have a very basic question about random sampling.
Consider the following:
Population
Population size
Sample size
Male
1200
200
Female
800
200
Where each sample for the population partitions have ...
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glm result difference between multiple number of bootstrap simulations vs. one simulation based on mulstiple boostrap measurements
I am performing 1000 bootstrap iterations on my data. For each iteration, there are 18 measurements included in the sample, and I would perform GLM after each iteration. So in total, I get 1000 GLM ...
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Resample multi-variate data to uniform density
Problem statement
I have two multivariate data sets A and B. For each item in the A, I want choose one from the B to create a pair $(a,b)$. I find candidates from the second data set by the criterion $...
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Is a "trimmed" simple random sample still a simple random sample?
Suppose I have a population of size N (N is large, say a million), and I take a simple random sample of size 100. Denote the units in the sample as $u_1$, ... $u_{...
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Resampling small datasets - Issue of overcounting?
Hypothetical situation here, as techniques like bootstrapping often fail for very small datasets.
Taking bootstrapping as an example nonetheless. we can easily compute the number of possible bootstrap ...
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Bootstrapping allows retraining across the different bootstrapped datasets? [closed]
I am training a model and I would use bootstrapping since my dataset is really really small. Hence, I bootstrap a dataset, I train on it and then I get some validation error and metrics on the unseen ...
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Significance testing the difference in dimensionality between conditions
I would like to test the hypothesis that under two experimental conditions there is a difference in data dimensionality.
Measures of dimensionality are complicated statistics of the data; for example, ...
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Bootstrapping example ISL - pages 194-195
I'm currently learning about bootstrapping using the book Introduction to Statistical Learning, and am struggling to understand what the point of using the boot ...
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How does pseudoreplication differ from resampling
I wonder how pseudoreplication (https://en.wikipedia.org/wiki/Pseudoreplication) differs from resampling (which is simply resampling with replacement from a given sample).
On the one hand, resampling ...
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What is the theoretical justification of testing for equal mean/distribution via resampling?
If we have two data sets $X_1,\ldots,X_m$ and $Y_1,\ldots,Y_n$, each i.i.d., and wanted to determine whether $\mathbb{E}[X_1] = \mathbb{E}[Y_1]$ or not using $\bar{X}_m - \bar{Y}_n = \hat{\Delta}_{m,n}...
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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 ...
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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 ...
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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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Cross validation and undersampling
I have a dataset with very skewed distribution (approx. 90 with class 0 and 10 with class 1).
I have considered to use undersampling to reduce size of the majority class.
I would like to know the ...
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