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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Resampling procedure for unequal sampling units
I’d like to know if the diversity of waterbirds affect zooplankton diversity in natural temporary pools over two seasonal periods (wet and dry). I’ve sampled zooplankton in 38 10x15 cm plots on the ...
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Bootstrap to test differences between correlation coefficients
I have two correlation coefficients ($r_1$ and $r_2$), obtained within the same sample (20 subjects). My aim is to test it they are significantly different.
$r_1$ is the correlation between a ...
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Bootstrap inference
I got a historical set of returns and use them on different investment strategies. From each of these strategies i calculate some measures like terminal wealth, growth rate etc.
Now to get a sense ...
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Sampling distribution need not be bell shaped?
Considering the following sample $X = $ [32.5, 31.7, 29.2, 28.8, 29. , 28.9, 29.9, 30.4, 26.9, 26.5]
I planned to evaluate the hypothesis of this sample having been generated from a normal ...
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Bootstrap resampling
I have a question with regard to non-parametric bootstrap re-sampling analysis.
I have a sample of n=200 values, and I have performed a bootstrap re-sampling creating 10.000 samples of the same n=...
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'Order of Operations' for preprocessing in special grouped variables case
My question relates to the order in which preprocessing steps occur.
The dataset I am working with has a group of special variables. They are each a separate column in the dataset, but it is known a-...
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Logistic Regression with Unbalanced Data [duplicate]
I have a binary dataset which is 99% in one class and 1% in the other class. I MUST create a logistic regression. I have read literature that says both using this dataset as is, or over/undersampling ...
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How to deal with multiple "runs" when resampling data
I have some code that does the analysis and creates figures and tables for a publication. It is integrated into my LaTeX build process. Part of the analysis includes a bootstrap comparing the ...
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Which randomization test is equivalent to bootstrapped CIs
I have used bootstrapping (percentile method) to calculate the confidence interval for the estimated mean of a set. I have now divided my data into two groups (a and b), and I want to test if the mean ...
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Bootstrap - resampling - two levels - small number of subjects (n = 3)
I collected data from 3 rats ($r_1, r_2,r_3$). Each rat was put under two treatment conditions. Under each condition, from each rat, I collected responses of many brain cells (neurons, $n = 150-200$) ...
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Which resampling methods are suitable in the presence of dummy variables?
I want to build a machine learning model using the caret package in R. Some of the features in my dataset are dummies taking the value 0 or 1. I would like to know which resampling methods can be used ...
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want to calculate significance of pairwise sharing between lists. Standard hypergeometric test seems inappropriate
I want to calculate if the percentage of sharing of strings (genes in this case) between lists is significantly more than expected by chance for multiple pairwise comparisons between lists of strings. ...
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Regression sensitivity analysis by re-sampling duplicates
In R, I have completed a simple regression of the form lm(Y~x+a). The dataset original dataset included several non-independent data points, which I selected among using a set of rules. I want to test ...
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Resampling in R to see if percentage of pairwise sharing between lists is significant
I have three lists of strings: ListA, ListB and ListC.
First 3 lines of ...
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Estimating coefficient of variation with small sample size
Problem: Estimate coefficient of variation for the mean expenditures for a number of groups. For each group, I have a sample of totals and the counts of people. E.g., for group A, I have totals (...
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Are SMC samples uncorrelated?
In this paper the authors list advantages of SMC. One of them is:
Unlike MCMC, SMC particles are uncorrelated and do not require the
determination of a burn-in period or assessment of convergence....
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sampling/importance resampling - why resample?
I'm trying to understand the SIR algorithm. In order to do so it seemed best to look at some existing r code (see below), taken from http://hedibert.org/wp-content/uploads/2013/12/example-iii.R.txt.
<...
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What is the difference between jackknifing and LOOCV? [duplicate]
Leave-one-out cross-validation model technique is very similar to jackknifing resampling, because both omitting each training case and perform retraining of the network on the left-out subset.
On ...
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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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When should the Pasting ensemble method be used instead of Bagging?
Pasting and Bagging are very similar, the main difference being that Bagging samples with replacement (which is called "bootstrapping") while Pasting samples without replacement.
I am guessing that ...
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Cross validation function from which programming language is more appropriate? [closed]
I'd like to use resample to achieve stable results of an unsupervised algorithm that finds clusters in data. I'll use k-fold cross validation repeated many times but I'm in doubt if I should use R or ...
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Subsampling to determine a standard error, how does it work?
I need to calculate the standard error on a complicated dataset (> 1700 records) which uses genetic matching. Using bootstrap results in very high computation time (because of the genetic matching)....
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Resampling pre - post data by shuffling pre/post
Is the following approach a valid way to use resampling to determine if observed changes in pre/post test are significant:
The null hypothesis is that there is no difference between the pre and post ...
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Compute confidence interval for univariate Kernel Density Estimator
I've got a univariate dataset (timeseries) for two kind of simulated systems, and I want to explore the differences between the two.
To do that, I can build a univariate gaussian KDE for each dataset ...
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Can you acquaint a randomization test with any test?
I want to determine whether my randomizer works properly in a statistical test. One method to do this is a randomization test where we try all possible combinations for two groups. For example: could ...
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How do I show that the two methods of permutation test are both valid?
My main objective is to show the methods described below are really the same. However, I am having difficult both formulating the idea clearly and proving my assertion. Below is my attempt.
Suppose ...
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log transform vs. resampling
I want to run a simple regression predicting score on some task, from number of minutes spent doing another activity. My N is ~800. The score variable is normally distributed and measured in ...
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How to Sample from a Randomisation Distribution?
I will use an example to explain my problem, but I think that it can be generalised to any randomization testing that does not allow one to create the null distribution by enumerating of all the ...
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Distribution of shuffled elements in permutation test
I've tried to find an answer to this problem by searching online, but I couldn't, so please forgive me if this question has been asked and answered. :)
I have two vectors of length ...
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How to generate a more accurate distribution of sample of random variates?
I want to generate a sample of random variates choosing 5 out of 7 days of the week, $X_1,\ldots,X_5$, such that the aggregate counts resemble a given day-of-week profile, namely
$$\mathbb{E}[\mathbb{...
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Modeling rare events
I am looking to model fraudulent cases using logistic regression. However there are tow different datasets which are available.
I used to build my model on; it had 4% of fraud cases. My model on this ...
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In R package caret, how is linear regression model trained by using resampling?
Resampling is usually used to find the best tuning parameters for a model. However, for some models, such as linear regression model, there is no tuning parameters. In this case, what can we get from ...
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Quantum computing and resampling techniques
Maybe I miss interpreted how does quantum computing work.
If I understood well it would allow to perform extreme parallelization by making using a single qubit to perform many calculations at the ...
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Sample Representing a Different Population
I have two sets of populations: containing 1.5 million and 5.5 million units. I need to select a sample out of 5.5 million population so that the sample represents the 1.5 million population based on ...
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Difference between bootstrap and resampling
I am using biological / microarray data. For example, one of my datasets has 50 samples, and 1000 gene attributes. They have 2 labels, Normal and ...
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Cross-validation vs random sampling for classification test
I usually have used cross-validation for testing classification performance. However, I read about the article that random sampling (bootstrapping) works better in many cases. I am not sure which one ...
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Confused about the sample size in large sample confidential interval and population proportion intertal
My professor said when sample size is large, it will has the following formula, but how big is the sample can be considered to be a large sample and use the following formula?
I did some questions ...
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Difference between monte carlo based analysis and a hypothesis test
Is it reasonable to use Monte Carlo methods to resample a dataset of weekly rainfall amounts to statistically test for difference between two timeseries? That is, randomly pull ~30 paired observations ...
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Applying SMOTE and PCA to high dimensional data giving low accuracy
I have a high dimensional datasets of around 2300+ columns. The dataset consist of two class labels of which one is extremely biased and occurs less than 10%. I looked at the various algorithms and ...
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Artificially adding more points x, y in linear regression
If I have $x$, $y$ data pairs and I am fitting $y = ax + b$, so I am using a standard OLS model. My questions are:
If I take a particular value of the tuple (x,y), say $x_5$, $y_5$ pair and add it to ...
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Creating a bootstrap null (H0) distribution. Investigating alternatives
I'm investigating the possibility of creating a non parametric bootstrap null distribution for hypothesis testing in multivariable regression analysis.
The null hypothesis is based on the absence of ...
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How do I bootstrap with R correctly without increasing the degree of freedom?
I need to compare survey responses from my experimental (referred as "FS" below) and control group (referred as "GV" below) using independent samples t-test. As the two groups have unequal sample ...
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Resampling under the null versus the alternative hypothesis
I'm looking at a community of organisms using simultaneous GLMs via the mvabund package.
The manyglm function from this package ...
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Is training error ever "good enough" when we have a large enough sample?
I recently had a (more experienced) coworker tell me that when you have a large enough sample, training error should be "good enough" to assess model performance. His point was that with a ...
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How to re-sample particle filter's particles for a 1D door/wall problem
So assuming your implementation of the motion model and sensor model is at a somewhat satisfying level, the question then is how do I stabilize localization with the re-sampling step.
I'm currently ...
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Resampling for comparative purposes
I'm here with a question concerning resampling. My issue: I measured the stature of an individual and found him markedly tall when compared with other eight subject from the same population. However, ...
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Standard error of an estimate from a non-probability sample?
Assume one tries to estimate the mean price of an item by employing a non-probability sample due to the lack of a survey frame of the universe of all items. How would I go about calculating the ...
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Bootstrap methodology. Why resample "with replacement" instead of random subsampling?
The bootstrap method has seen a great diffusion in the last years, I also use it a lot, especially because the reasoning behind is quite intuitive.
But that's one thing I don't understand. Why Efron ...
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Updating the head or tail of an ordered subset of rows using data.table in R [closed]
The concept is simpler than my title. I have a data.table that represents a sample taken from a population. My goal is to test the performance of several different prediction algorithms across sample ...
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Estimation on evolving distribution with small updates
I have a set $X$ of $10^6$ elements and a time series of probability distributions $\mu_1,\mu_2,\ldots$ on $X$. I want to estimate the expected value of a function $f$ over each $\mu_t$. It is easy to ...
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