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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Variance of permutation test p-value?
The one-sided p-value for permutation testing is $ \tilde{p} = (r + 1)/(n + 1)$, where $n$ is the number of permutations and $r$ is the number of p-values that are equal to or greater than the p-value ...
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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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14 views
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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9 views
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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19 views
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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20 views
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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39 views
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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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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30 views
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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9 views
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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55 views
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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1answer
27 views
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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54 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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23 views
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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12 views
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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1answer
65 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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1answer
38 views
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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15 views
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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39 views
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 ...
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33 views
bootstrapping covariance matrices with different sampling procedures
The regression model has heteroskedasticity. The variance of error term depends on regressors. From boostrapping analysis, I got two different covariance matrices of $\beta$. The difference results ...
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1answer
40 views
different p-values from resampling and traditional methods
I'm not sure if this is hard to answer without the original data, but let's give it a go.
I'm calculating the difference in proportions for two diagnostic methods:
...
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26 views
bootstrap correlated data intuition
To estimate the variance of a parameter, I know that a simple bootstrap will not work for correlated data. I also read about block bootstrap. I am trying to get an intuition about why it is necessary ...
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Resampling imbalanced data in a multi-view scenario
Assume a multi-view scenario, where multiple views of the same entity are available. If each data pair is assigned a label and the resulting scenario is highly imbalanced, what are proper ways of ...
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24 views
Enzyme kinetics: Bootstrapping parameter estimates
First things first - this is kind of a mixed problem from biological data analysis, I hope I am in the right place to ask my question(s).
Context
I have data from a fluorescence assay of binding of ...
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44 views
Can we sample from a set of biased samples to get unbiased samples?
This is a follow-up question to How to uniformly sample vertices from a large graph with given distance from a fixed vertex?.
Suppose I took a set $B$ of $n$ samples from a large but finite universe $...
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1answer
87 views
Process for oversampling data for imbalanced binary classe
I have about a 30% and 70% for class 0 (minority class) and class 1 (majority class). Since I do not have a lot of data, I am planning to oversample the minority class to balance out the classes to ...
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1answer
48 views
Precision and recall of imbalanced classes
I'm new and have searched many questions about this problem in this stack, but those answers aren't clear enough for me.
The point is the area under PR curve of my binary classes is the same as the ...
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391 views
SMOTE, Oversampling on text classification in Python
I am doing a text classification and I have very imbalanced data like
Category | Total Records
Cate1 | 950
Cate2 | 40
Cate3 | 10
Now I want to over ...
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1answer
13 views
Is there any point in splitting data before applying NHST?
If I have understood correctly, Null Hypothesis Significance Testing is a device, which eats data and a conservative guess, and outputs a 'probability' that the data was generated by the hypothesized ...
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1answer
379 views
Oversampling: whole set or training set
I have a rather small dataset of 4 000 points (140 features) to feed to a NN binary classifier. The problem is only ~700 of them represent the second class. Is it more common to resample the whole ...
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48 views
“ANOVA on a non-random non-Normal sample from a Normal Population”
How can I run ANOVA or tests for statistical significance on a bi-modal sample that came from a normal population?
Context:
I was tasked with running an ANOVA to see if genotypes (treatments / ...
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1answer
39 views
Hypothesis testing on subsamples and cross validation
When working on very large datasets, identifying effects rests more on quantification than significance and many questions/answers give insight on what to do regarding large samples like this (very ...
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1answer
146 views
Conceptual questions on ensemble learning and Boosting methods in Matlab
The documentation on ensemble methods in Matlab explains different ensemble algorithms for classification and regression tasks. I have normalized the raw feature set and using the normalized data for ...
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48 views
Accounting for sensitivity of model to small changes in assumptions
I am running a Monte Carlo simulation of correlated asset returns, based on this Matlab function.
The model's inputs are a vector of expected means and standard deviations for 9 assets, and their ...
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63 views
Bootstrap Resampling Vs KDE Resampling
Let $\xi\in\mathbb{R}^{m}$ be a random vector with joint desity function $f$, and let $\widehat{\xi}_{1},\ldots,\widehat{\xi}_{N}$ be a sample of $\xi$. We have that the kernel density estimator (...
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21 views
Resampling methods in derivation of prediction error
I have made a model which predicts some value $Y_i$ given $X_i$ where $i \in 1,...,N$. My ultimate goal is to predict $Y=\sum_i^n Y_i$ and to be specific the interval in which $Y$ lies within some % ...
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102 views
resampling from a frequency distribution (histogram) to test for significance
Suppose I have count data on brands and the number of toys they make.
...
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12 views
Using mean estimates of subsets of data or entire data to estimate a parameter
"Suppose that we wish to invest a fixed sum of money in two financial assets that yield returns of X and Y , respectively, where X and Y are random quantities. We will invest a fraction α of our money ...
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Optimal block size for spatial bootstrapping
Take a regression model:
$$
y_s = X_s\beta + \epsilon
$$
Where $E[\epsilon|X] = 0$, but $cor(\epsilon_s, \epsilon_{near}) > cor(\epsilon_s, \epsilon_{far})> 0$.
In other words, $X$ is ...
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40 views
For statistical resampling, why do we use Mean Squared Error (MSE) and not Residual Standard Error (RSE) to measure the test set error rate?
Is this as trivial as 'we use RSE for training-set and MSE for test-set'?
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1answer
286 views
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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1answer
81 views
Variance after resampling uniformly from a sample from a normal population
I have recently been looking into the Bootstrap, and I was wondering, if I were to have a sample $X=\{x_1,x_2,\dots,x_N\}$ that has $N$ samples, all i.i.d coming from a normal distribution, $N(\mu,\...
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1answer
20 views
Removing confounding variable when comparing distributions
I have the following situation and am hoping that someone can point me in the right direction. I am completing an experiment under 2 conditions (1 and 2) and measuring 2 variables (A and B). I am ...
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4k views
Why is bootstrapping useful?
If all you are doing is re-sampling from the empirical distribution, why not just study the empirical distribution? For example instead of studying the variability by repeated sampling, why not just ...
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325 views
What's the point of reporting bootstrap bias?
Suppose the data $X = (X_1,X_2,\cdots,X_n)$ is a vector of iid observations $X_i$ where each $X_i$ has marginal distribution $F(\theta)$. Suppose we observe $x = (x_1,\cdots,x_n)$ and $\hat \theta = \...
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85 views
K-fold cross validation: confusion over which model is actually chosen and validated
I am watching the online lectures of Machine Learning by Hastie and Tibshirani, which are quite good. But they discussed $k$-fold cross-validation and there is something confusing me. As they describe ...
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255 views
Replicate weights and the Current Population Survey
The Current Population Survey’s Annual Social and Economic Supplement (abbreviated, oddly, as ASEC) is the USA’s longest running and most detailed annual survey of income and employment. Along with ...
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184 views
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 ...
