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.

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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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Friedman's test over multiple dependent datasets

In 'Statistical comparisons of classifiers over multiple data sets', Friedman's test is applied to compare different machine learning algorithm performances over different datasets (obtained from ...
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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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Run a regression with data from different measurements

I have a population $N$, that can be divided into several samples. 1 sample, $S$, was taken out from $N$, let's say a piece of paper from a page of a manuscript; this piece of paper is divided into ...
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What is the typical size of feature matrix for xgboost

In other words, I have a binary classification problem with million samples and around 1000 features. I am trying to understand wheather I should subsample the dataset and add a feature selection step ...
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How often to subsample for classification?

It is often recommended to subsample randomly if class sizes are unbalanced in classification - especially when classification accuracy is used. My question however: How often should the subsampling ...
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How to: Normal sub-sampling out of a uniformly distributed data samples

Given a uniformly distributed sample of data, It's needed to sub-sample out the points in a Normal distribution fashion, i.e. more around mean and sparser as we move out. What could be the steps?
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testing for differences using jackknife distributions

I have two distributions relative to two experimental conditions. I compute a certain index (i.e. coherence) describing each distribution. I want to see if there is a significant difference ...
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What is a good introductory text on resampling methods? [duplicate]

I have found a few decent ones about specific resampling applications such as bootstrapped confidence intervals, but nothing broader. A journal article or book chapter would be preferable to an entire ...
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Choosing subsample size (helping a friend analysing a smaller data set) [duplicate]

A friend of mine is working analysing 2000 twits per day and categorize them as postive, negative or neutral. This is a really boring task but the algorithms that do this classification are not very ...
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Chance that bootstrap sample is exactly the same as the original sample

Just want to check some reasoning. If my original sample is of size $n$ and I bootstrap it, then my thought process is as follows: $\frac{1}{n}$ is the chance of any observation drawn from the ...
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Sub-sampling to quantify mean and errors

I have a model in matlab that, ultimately, outputs a yes or a no depending on certain input parameters. There's a degree of randomness in the model, so by runnning it 1000 times, I may end up with 200 ...
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Subsampling - Choice of Subsample Size?

I have a question with regard to Subsampling. Subsampling: take samples without replacement of size b from the original sample of size n with b < n Bootstrapping: take samples with replacement of ...
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Is it good practice to perform model parameter tuning on a random subsampling of a large dataset?

A lot of the datasets presented to us in the company at which I'm currently an intern are very large (many millions of rows / Gigabytes, or even Terabytes of data). While running machine learning ...