Questions tagged [subsampling]
Subsampling is a resampling procedure akin to the bootstrap in which fewer than all observations are being drawn with replacement (vs. the original sample size used in the textbook bootstrap method). For creating samples out of your existing data, please consider "sampling" tag instead.
80 questions
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Split up sample into two sub-samples while ensuring that subjects in sub-samples are not related
I have a sample that I would like to split up into two roughly equally sized sub-samples. These sub-samples should be similar concerning a defined set of variables (e.g. age, gender, motion, etc.). ...
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Hierarchical Clustering with Large Datasets
I am currently aiming to perform hierarchical clustering for the purpose of customer segmentation. My dataset consists of 217,000 instances with 12-15 features. However, due to memory issues when ...
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Best practice for subsampling training data and weights (in XGBoost)
I am trying to build an XGBoost model in pycharm and I have a general method question even though it relates to my model of choice (XGBoost). Any kind of general comments on the proper statistical ...
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Sampling 1 item from groups of correlated values and combining the statistics
We have a dataset consisting of several groups of observations:
Group
Object
Value
Gr_1
Ob_1
V_1
Gr_1
Ob_2
V_2
Gr_2
Ob_3
V_3
...
...
...
All values lie in the interval [0,1].
In each of the ...
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Block size in subsampling and bootstrap for time series
I have a dependent variable, a time series of 80 periods (discrete decisions). I am doing maximum likelihood estimation with 10 parameters. Now I want to get the standard error or confidence interval ...
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Mixed effects model in R with subsampling in replicates
I am working on a mixed effects model in r where I have 2 treatments, 5 replications and subsampling with each site. I dont have access to the raw subsampling data though, I just have the mean effect ...
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XGBoost's subsample = 0?
In my use of XGBoost with the gradient-based method, I inadvertently set subsample to 0, yet it surprisingly returned a good result.
I am not sure how to explain it well. Any idea from the community?
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Sub-sampling categories for fisher's exact test
Can I sub-sample categories before running Fisher's exact test?
I have a 50 x 22 data matrix (50 categories and 22 samples), where some sample values are < 5, so I can't run a Chi Squared test. I ...
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Using t-test to check the difference between a sample and subsample? [duplicate]
I have a large dataset and I want to check if there is any difference between the whole sample and the subsample.
For example, I'm checking the difference in the age of patients in the whole sample ...
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In practice, should I use k-fold cross-validation or repeated random sub-sampling validation as my default choice of evaluating the model performance?
I was wondering if someone can shed some light on which cross-validation method should I, in general, use more often: k-fold cross-validation or repeated random sub-sampling validation.
From Wikipedia,...
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Which training instances are being subsampled in each iteration of XGBoost?
The subsample option in XGBoost is described here as follows:
Subsample ratio of the training instances. Setting it to 0.5 ...
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Variance of the mean following multiple steps of subsampling
Let's say we have 10 sets of 1 to 4 values each. First we sample one value from each set, giving us 10 values. We then sample five of these. Lastly, we compute their average.
If one repeated this two-...
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FWER adjustment
I want to use the built-in ToothGrowth dataset in R to write an assignment and conduct t.test...
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How to take sample of data from very unbalanced DataFrame so as to not lose too many '1' for Machine Learning in Python?
I have DataFrame in Python Pandas like below with ID and Target variable (for machine learning model).
My DataFrame is really large and unbalanced.
I need to make sampling on my DataFrame because it ...
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What does "subsampling at 5%" mean?
This from the description of a kaggle competition:
Note that the negative class has been subsampled for this dataset at 5%, and thus receives a 20x weighting in the scoring metric.
They don't ...
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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 ...
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Representative Observation and Random sampling
Suppose that we have a cross-sectional data set drawn by random sampling.
Then, the data may represent our population of interests.
We can consider two subpopulations: male group and female group.
...
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Subsampling For Class Imbalances and no-information rate
Question has to do with the interpretation of output of the caret package .
Subsampling (either up or down) is set up in caret ...
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What we should do when the result is only significant to one country but the whole sample?
In a staggered Difference-in-Differences setting, Dasgupta, 2019 has a formula for such a static setting is
$Y_{it}$ = $\alpha$ + $\beta$ $pt_{kt}$ + $\delta$$X_{ikt}$ + $\theta$$_t$ + $\gamma$$_i$ +$\...
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Subsample analysis based on country-level indices?
In a generalized Difference-in-Difference setting from Dasgupta,2019 for multiple event dates (laws staggered implementation)
The baseline equation:
$Y_{it}$ = $\alpha$ + $\beta$ $(Leniency Law)_{kt}$ ...
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Knowing when to stop with spatial point thinning -- is an approach with Clark-Evans test valid?
I need a random sample of presence points from the species' area to use in distribution modeling. I have an excessive amount of spatially clustered presence records, so I use minimum nearest neighbor ...
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Linear regression in very unbalanced data
I hope you can help me with this question. I have a dataset with several classes (around 25) and they are very unbalanced. Some classes have thousands of subjects, others hundreds, and others just a ...
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Subsampling the "right" amout of data to train an ML model
I am training a machine learning model (i.e., a classifier) on a large dataset. I know that I can get the same results using less data (about 30%) but I would like to avoid the trial and error process ...
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Distribution of a rate in a subsample after permutation of the total dataset
I need to estimate the distribution of a rate in a subsample after permutation of the total dataset. I was wondering whether it's sufficient to use the binomial distribution with the global rate ...
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How to treat apples and oranges best in multiple regression analysis? One fits all regression model or separate sub models? [duplicate]
I have a data set ready for multiple regression analysis that consist of apples and oranges. Let's say the depended variable is fruit size and there is a bunch of independent variables (categorical &...
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Combining Sub-Samples for Factor Analysis?
I am a newbie on the site and a relative newbie to some of the analysis I am trying, so my apologies in advance for any rookie mistakes or for asking what might be obvious to others!
Can I run a ...
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Estimate linear regression coefficients and standard errors using sub-samples of dataset
I am working with a very large dataset (250 million records) and I want to use linear regression to estimate how some variables are related to the outcome variable. I have a lot of categorical ...
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166
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Finding a sub-population from dataset matching another target dataset
Let's say one has a finite collection of i.i.d. samples from an unknown source distribution $S=\{x_{i} | i \in [1,n_{S}], x_{i} \sim p_{X_{S}}(x)\}$. Where each $x$ is multidimensional and has ...
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Speed Up KNN and Maintaining Accuracy for Anomaly Detection
This question is about using KNN in the context of anomaly detection. If the training dataset is large(10 M data points), KNN will be slow. Is subsampling(i.e. use a small subset of original training ...
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Time series forecast where each measurment is already averaged and has a spread
I would like to forecast a time series consisting of time averaged (everything happening during 15min intervals is averaged and recorded with a timestamp of the start of messurment) quantities (...
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Intuition behind m-out-of-n bootstrap
I am trying to get some intuition on why m-out-of-n bootstrap works but haven't been able to find good explanation. I would really appreciate any input on this.
I think I do understand what bootstrap ...
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Can you use the isolation forest algorithm on large sample sizes?
The original isolation forest paper states that the algorithm works best on small subsamples, but is it okay to use it on large sample sizes or are other anomaly detection algorithms better?
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How to partition a sample into representative subsamples?
The problem is the following: take a sample $X$ of the general population $\Omega$. Each element of $\Omega$ (and hence, each element of $X$) is described by a vector of characteristics, each ...
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Fitting glmms to data with very unbalanced sampling effort - could subsampling help?
I have a data set comprising measurements of invertebrate species richness from grab samples of seabed sediment collected from a shallow coastal area over a period of 20 years. The sampling effort is ...
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Subsampling large dataset before testing?
I'm running a Kruskal test with Conover post-hoc test to assess if there is a statistically significant difference between a numerical and a categorical variable with R.
I previously created boxplots ...
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Regression - Interpretation of coefficients and probability
I am very confused about the output of my regressions.
First of all, I am not even sure if I could divide my sample as I did, meaning that by subsampling as I did the variable ESG score is both ...
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Techniques to account for differences in misclassification "cost" on variables other than the outcome
Suppose you're in a classic classification context: you want to predict whether a patient has a certain virus. You are working in multiple regions (let's say 2 for simplicity: Region A and Region B) ...
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Covariance between sample mean and subsample mean
To estimate $\overline{Y}$(population mean), suppose an SRSWOR of size $n$ is taken from a population of size $N$, and the sample mean $\overline{y}$ is calculated. Then an SRSWOR subsample of size $...
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How to compute ESS (Effective Sample Size)?
I implemented the ESS calculation according to this manual like this:
...
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appropriate error term for a randomized complete block design, possible split plot in time, with subsamples (in SAS PROC MIXED))
I am analyzing the results of an agricultural experiment, as follows:
Plots are laid out in a randomized complete block design with 3 replications. They have been either conventionally or organically ...
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How to perform inference on stratified sampling data
Let's say I'm studying a population of generic emergency calls to over the course of several months, and keeping track of the following independent variables:
month (when the call happened)
country (...
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Finding the variance of subsample-based estimation
Say, we know that the probability of an object having some property equals exactly $P$. We are given a sample (of size $N$) of these objects - in fact, that is a Binomial distribution with probability ...
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Presenting results of estimation with sub-sampling
I have a dataset that we can partition between three groups: Controls, Treated 1 and Treated 2.
I want to run regressions that include the whole Control and Treated 1 groups, but I draw a random ...
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Do both Bootstrap with and without replacement create a distribution?
I'm having a "noisy debate" with colleagues about whether sampling without replacement can still create a distribution.
Methodology:
A bootstrap (iterative process where I calculate Somers' D for new ...
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Can I take a random sample of my very large data set to overcome non-independence?
I am trying to run a regression model on a very large time series data set (comparing flow noise to vehicle speed, pitch and dive state). Because my samples are taken about every minute (with some ...
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Subsampling as a method for time series train/validation splits
I have a question concerning train-test splits for time series data:
Background
I have a dataset of sensor data points for 13 month with datapoints measured every 5 minutes which I downsample to ...
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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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Does sampling from a large dataset lead to correct inferences?
Say we have some population, and we obtain a "representative" random sample of that population, $(y_i, x_i)_{i = 1}^n$, where $n$ is very large (millions) and $x_i = (x_{i1}, x_{i2}, ... x_{ip})'$ is ...
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Statistic to Verify Subsample is Similar to Original Sample?
I have a subsample of data (120 students) that was taken from an original sample of 1,216 students' data. I need to report in my manuscript whether my subsample's key demographics (age, gender ...
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Subsampling to account for spatial autocorrelation of observations
I'm wondering to what extent (if any) subsampling of observations can be used to account for spatial autocorrelation within data.
Is taking a smaller sample (subsample) of observations (without ...
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