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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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KDE that better-preserves percentile distributions

My understanding is that a Gaussian KDE, because the kernel is symmetric, preserves the statistical mean of a distribution. For my particular case, I'd really prefer a KDE that preserved the median, ...
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Correct approach to define a null distribution by resampling?

I am trying to devise a test for significance by resampling from my data. The data consists of 60 individuals, grouped in 6 populations (10 per population). There are about 9,000 genes as ...
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Central limit theorem for resampling

What is the analog of the central limit theorem or concentration theorem for resampling, say, an i.i.d. samples? Are there any references for this topic? As a simple example, suppose there are $n$ i....
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what are the assumptions of permutation test?

It's often stated that permutation tests have no assumptions, however this is certainly not true. For example if my samples are somehow correlated, I can imagine that permuting their labels would not ...
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Is there any way to resample in order to change the distribution of a sample?

I ran an experiment that involved an independent variable which is known to have a right skewed distribution. However, in the experiment, the independent variable was distributed differently between ...
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Resampling technique to compare genetic diversity indices

I would like to know what would be the more appropriate way to compare two gene diversity indices by resampling or bootstrap methods. The data are as follows. In the first sample, I have 23 ...
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Generating new samples from dataset to expand dataset

I want to choose one dataset and then expand the dimensions/number of samples to show how a dimension reduction method(not yet decided) reacts to changes in dimensions/number of samples. My plan was ...
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Can one construct “original” data from a function of jackknifed data?

Say I have original, uncorrelated data, $x_i$, with $i = 1,2 \ldots N$. I can jackknife this data set (a simple delete-one) $$ \bar{x}_{i} = \frac{1}{N-1}\sum_{j \neq i}x_{j} \quad\quad (1) $$ to ...
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Resample dataframe by random subset of years

I have a dataset of 59 years worth of daily rainfall data. I'd like to resample the data by 6 randomly selected years 1,000 times, using the entire year of data for each of the 6 years (so 2190 ...
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How to calculate p-value comparing bootstrap-based predicted probabilities and observed probabilities

I posted this question on stackoverflow first but I have got no answer so far, so I decided to post it here in the hope that here I might get an answer. I hope my procedure is acceptable. ...
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When to use ordinary, balanced, antithetic, or permutation resampling for bootstrap?

I am using boot and would appreciate any explanation as to when using each of these resampling methods would be recommended in practice. I have come across Do who says that: Simulation results ...
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How does one estimate error of extrapolated value with only 2 data points?

I am trying to do a simple linear regression. I have only two data points with errors. How do I estimate the y-intercept with errors? Currently I have: ...
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Correlating Two Time Series with Gaps in Data and of Different Lengths

I am attempting to correlate the time series from 4 separate tilt monitors that sample every 5 minutes. The time series have slightly different base times and end times, and the resulting arrays are ...
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1k views

Resampling correlated data using bootstrap

I have a dataset of $n×m$ numbers, where the $m$ variables ($m$ is in the order of 5–10) exhibit various degrees of correlation with one another. The format of the data is not necessarily a timeseries,...
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Bootstrap, Rubin's rules, and uncertainty of sub-estimates?

Can someone provide an intuition for why, when using bootstrap to calculate the variability of an estimate (say a regression coefficient $\beta$) we don't need to incorporate the uncertainty of each ...
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Selecting uncorrelated samples from a set of bulk data that contains correlated and dependent samples

i have a set of data that is generated by expensive computational model evaluations, on a total data set of 10000 samples in 40 dimensions. This sample data set is composed of different data sets, ...
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Appropriate visualization and statistical testing using resampling stats

I'm using a linear SVM to perform classification on a dataset. It seems to me that there are many ways to visualize my results and report their statistical significance, and I'm unsure of best ...
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Adjusting predicted probabilities after resampling

Suppose I've got highly imbalanced data and I want to train a model, for binary classification. So I upsample the minority class or downsample the majority class or whatever. My question is whether ...
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Permutation tests for paired data with several variables

I am trying to answer my own previous question, with a permutation method. I have a reference method $M_{ref}$ that I would like to compare with other methods $M_1$, $M_2$, $M_3$ when taking ...
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Calibration curve for mixed model (logistic) [closed]

I did some searching using the CV search function and was unable to locate any information on R packages or general approach to creating a calibration curve with a bootstrapped curve overlay (similar ...
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How should I resample the training and testing set with imbalanced data whilst having meaningful performance metrics?

I have an imbalanced dataset of approx. 200 positive and 800 negative examples. I run nested cross-validation where i=5 and j=5; (i is inner and j is outer). The cross-validation procedure isn't the ...
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Bootstrap test of means using pooled groups - is there a problem?

In his book "Resampling: the New Statistics" (available for free online), Julian Simon presents several examples where he Bootstraps the differences in group means, assuming that the observations of ...
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How to choose resample size when drawing without replacement?

Say I have some second-order statistic $m(x)$ where $x$ is a data vector of length $n$. Let's also assume that the limiting distribution of $x$ is gaussian-ish, but generally unknown, so that the ...
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Monte Carlo testing: number of required permutations

I want to perform a statistical hypothesis test, however I don't know the exact distribution of my test statistic under $H_0$. Therefore, I need to calculate a Monte Carlo estimate $\hat{p}$ of the ...
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246 views

Reduce 'oversampling' when using bootstrapping

I'm wanting to use bootstrapping to estimate the sampling distribution of values over a geographical area. However, the samples I'm using are more heavily sampled in some areas that in others, for ...
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Adding Bad Events from the past to the risk default model to avoid Down/Up sampling techniques

We have been trying to build a classification model for credit default prediction using two different models one being Random forest and another being the Logistic regression based scorecard model. ...
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how to use boot package to do stratified bootstrapping?

Here's a toy data set that replicates my problem. I am interested in knowing the confidence intervals of an empirical distribution that is composed of the scores of each school at the proportion that ...
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Hypothetical sample size

I have a data set of n=98 of values that is normal distributed. This data set has a certain spread and hence a certain standard error of the mean. I now want to test how many samples (ns) I would need ...
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Practical questions about cluster bootstrap confidence intervals

I want to estimate the accuracy of a machine learning model. I have an independent test set with a vector of trusted labels and a corresponding vector of model-based predictions. If I assume the test ...
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How to approximate continuous function from discrete uneven interval value?

To clarify, I have a discrete value of an unknown distribution. The value of this distribution domain is from 0 to 1. But the value which the discrete value is sampled has an unknown interval. For ...
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Is Tomek Link undersampling the same as Edited Nearest Neighbours with 1 neighbour?

From what I've read I've understood that undersampling the majority class with Tomek Links or Edited Nearest Neighbours with 1 neighbour should yield the same result. However, I've tried it on this ...
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What are RMSE SD and Rsquared SD metrics in resampling results using R package:caret?

I've been doing predictive modelling with R package caret. When resampling regression models, I get the traditional RMSE and Rsquared metrics, but also RMSE SD and ...
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General way to construct a confidence interval for a unknown constant to which a sample estimator converges

Assuming that a sample estimator converges to some unknown constant (a wild assumption to be sure) and without assuming the distribution of either the sample estimator or the variables from which it ...
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1answer
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ROC and PR curves after over/under sampling in Unbalanced datasets

As I understood till now, ROC curves are not a good presentation of unbalanced datasets and PR curves are preferred because ROC curves are not sensitive to false positives. If we now use resample ...
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203 views

How can we explain the fact that “Bagging reduces the variance while retaining the bias” mathematically?

I am able to understand the intution behind saying that "Bagging reduces the variance while retaining the bias". What is the mathematically principle behind this intution? I checked with few experts ...
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Application of Permutation Tests

I don't know how to post the question more formally. Therefore, let me introduce an example. Suppose you want to estimate the following regression: $n_i = f(x_i)+\beta \cdot 1[x_i = j]$, $n_i$ is ...
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Why with probability close to 1 bootstrap and classical tests provide the same decision? Is it caused by loss of pivotality? How?

Here is an example on which my question is based can please someone tell me what is the relation between the probability close to one and the fact that we have same decisions as classical tests and ...
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1answer
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Sample size calculation for Kendall's Tau in reproducibility study

Can anyone help with justifying (or rubbishing) a couple of aspects of a sample size calculation for a grant proposal. A pilot study gave significant results for the correlation of a continuous (non-...
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How to interpolate/resample both dense and sparse points?

Suppose I have data like red points below I would like to interpolate/resample these points at black ticks. At right the points are sparse and it is obvious to interpolate them linearly or with ...
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Dealing with dataset imbalance: test if adjusting is necessary

I'm currently working on a project which uses a imbalanced dataset (two classes) for training, and I'm not sure if I should do a resampling procedure or not. Is there a way to actually test if it's ...
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How to bootstrap data with unknown correlation structure?

I am interested in how to validly bootstrap data with an unknown correlation structure. Let's say I am bootstrapping in order to obtain inference for some smooth function of the data similar to a ...
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Mean of the sampling distribution of the OLS estimator

Suppose b represents the OLS estimator, and B the true coefficient in the regression model without intercept y = Bx + u. Under certain assumptions b is unbiased ...
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1answer
66 views

Hypothesis testing when you have the entire population?

I have an experiment that involves testing the route-finding ability of 3 different critters. They have to travel between 5 different points (essentially a travelling salesman problem) and for each ...
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SVM performance drops in validation

I'm checking my ability to predict a binary outcome using SVM. While the model fits very well, when resampling the model performance drops (but not so much when using logistic regression) which makes ...
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How would you do a two-one-sided test with resampling (bootstrap)?

Say I have two samples and I want to test whether their mean difference is likely to be in the interval [-0.1, 0.1] (this is, I want to both reject the hypothesis that it is smaller that -0.1 or ...
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Test for IID sampling

How would you test or check that sampling is IID (Independent and Identically Distributed)? Note that I do not mean Gaussian and Identically Distributed, just IID. And idea that comes to my mind is ...
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1answer
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Paired-t test for comparing two subgroups selected from the same underlying population?

Say I have a total population of N=100. Algorithm #1 is based on some predictors and it selects n1=10 subjects (i.e., subgroup #1). Algorithm #2 is based on another set of predictors and it selects n2=...
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Resampling to get equal predictive power per observation

Cross posted from data science due to lack of response This is probably a thing I am just not searching for correctly, but essentially my idea is this: given some machine learning classification $C$ ...
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Handling imbalanced data for classification [duplicate]

What are the best ways to deal with imbalanced datasets for classifying whether or not individuals pay their tuition? The data is 75% positive class (paid) and 25% negative (unpaid). Some approaches I ...