Questions tagged [stratification]

A sampling technique in which the population of interest is partitioned into subsets ("strata") based on characteristics known at all units before sampling.

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How to show that $n_h $ is proportional to $\left(\frac{W_h^2S_h^2}{t_h}\right)^{\frac{3}{2}}$?

If cost function is given in the form $C = c_0 + \sum t_h\sqrt{n_h} $, where $c_0$ and $t_h$ are known ($t_h$ - transport costs to survey one element in a particular strata). Show that minimising $D(\...
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How to calculate and compare ML models Recall estimators from stratified sample?

Let's say that I have 2 machine learning models for a disease (binary) classification task. I would like to estimate Recall for both models and compare it (show statistically that for example Recall ...
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Confidence Interval around a single strata's proportion in a stratified random sample?

I am conducting a stratified random sample of properties in 10 cities of a binary attribute (has fence yes or no). This is using remote sensing so no response rate, it's me inspecting images of ...
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Stratified shuffle for normally distributed target variable

When splitting data for a classification problem one is advised to use stratified shuffling in case the target variable is skewed toward a certain class. Indeed, Sklearn has a function for that. ...
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What is the optimal sampling split for stratified sampling?

Suppose we have a population split into two strata of interest and we want to estimate the mean of some quantity of interest pertaining to the population. Denote the quantities of interest for the ...
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Conducting a Stratified Random Sample (Statewide Poll)

I am interested (in theory) in conducting a stratified random sample of $n$ registered voters from a state which has 150 counties. If I make all 150 counties my strata, must I conduct a random sample (...
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How to determine sample weights, when the prevalence in the population is based on percentages

The central question is: How to do I determine the weights, when prevalence in the population is expressed as percentages? I have posted this related question on Stack Overflow: According to the ...
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How to group countries with similar age distributions?

How can I group the world's 200-odd countries into (say) ten groups, with each group's countries having 'similar' age distributions? I want to compare COVID-19 fatality rates across countries. But the ...
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Can I read into the coeff or the effect size of independent variables in a mixed-effects logistic regression model if I did stratified sampling?

I am building a mixed-effects logistic regression model. I want to understand the impact of certain predictors. I am reading into the coefficients and the effect size (wald's statistic). One of the ...
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Determine sample size for each cluster

Suppose I have a large population and I want to test if installing a new special light bulb can reduce energy consumption. Since I have a large population, I'll have people that usually consume high ...
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Creating stratified subsamples from a sample (repeated observations) - Post stratification

I have a random sample of individuals in a country (without knowing in which province they live). From this sample I want to make stratified random samples on the provincial level (let's assume there ...
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How is stratified sampling better than sampling equally from all classes while crossvalidating?

I can see that stratified sampling helps in maintaining the same class distribution in the training set as in the original dataset. However, my understanding is that ideally, the model should be ...
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crossvalidation “balancing” for regression problems

Classification problems can exhibit a strong label imbalance in the given dataset. This can be overcome by subsampling certain class weight attributed weights, which allow for balancing the label ...
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What is a non-random k-fold cross-validation that is not aimed at representativeness?

I am working on a project where my dataset is structured as follows: for each observation, I have a concept translated in five languages, and a measure of word concreteness. So something like: ...
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Ex-post grouping blocks, when analyzing blocked randomization experiment, to avoid $N_b=1$ blocks

In an experiment I conducted, randomization was stratified geographically and then blocked by the baseline outcome variable, $x$. That is, within each strata, $s$, I grouped observations into blocks, $...
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Proportional allocation vs Neyman Allocation [duplicate]

I am trying to figure out how to use both allocations for an assignment and I am having a hard time figuring it out. More importantly, can someone explain how to use proportional allocation to me by ...
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Can oversampling be moved outside stratified k-fold CV?

In a binary classification task, I am using imbalanced-learn's implementation of SMOTENC to oversample the positive class of a very imbalanced dataset. The total number of examples is very high, so ...
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Why adjusting for randomization stratification factors in the model improves the precision of estimators for treatment effect?

I assume you will already get balanced treatment assignment within the randomization strata. Why would we still gain improvement in precision by adjusting for these factors in the model? Does not ...
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Age stratified analysis in R

I am working on a project for which I need to do age stratified analysis for age of onset, age at last visit to the hospital and age at death. Our collaborator wants me to create an "Age ...
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One random sample Mean or 30 Random samples Mean

I have 30m records and i want to do a random stratified sample to estimate the mean based on predefined strata. Should i estimate the mean while taking 1 random (n=500) sample or to sample lets say 30 ...
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Difference between stratification and subset

This may be a duplicate question asked differently, I am curious to know the difference between performing an analysis by stratification on a variable and by subsetting the dataset based on that ...
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Stratified sampling

I am learning about stratified sampling, and I have the following question: can stratified sampling achieve more precision than SRS if data is plentiful and the samples are big enough? For example, it ...
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Stratify Cox Proportional Hazard Model on continuous variable

I have a question regarding Cox proportional hazard models. I've been working with data with some time-varying variables and some that are fixed over time. In total 79 units are surveyed giving around ...
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When splitting a dataset into train/dev/test sets, how perform stratified splits on some labels but exclusion split on others?

Suppose I have a dataset of 1000 samples with label classes A, B or C that I want to split into train/dev/test sets in a stratified fashion. However, each sample has an origin metadata (o1, o2, o3, ......
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How do I calculate the survey sample size using stratified sampling?

Lets say I plan to do a survey of individuals aged 40-69 years old in 5 residential complexes. It is a general social survey, and is intended to survey attitudes towards a new cultural centre that ...
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How to set break values for a cross-basis function in R's DLNM package?

I am using distributed lag non-linear model in R for a time series data. I am forming a crossbasis matrix using following script: ...
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Stratified Sampling Total Statum

I wish to find the number of total population by each specification. I have the following results: \begin{array}{|c|c|} class & n_h \\\hline Positive & 5872 \\\hline Neutral & 6771 \\\...
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Stratified logistic regression with log link

I noticed the other day that I can mimic a stratified logistic regression using a meta-analytic approach at least when there is only one stratification variable with two different values. This is the ...
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Are the differences between sampling clusters and sampling strata, conceptual, methodological, neither or both?

I am fuzzy on the distinctions between sampling strata and sampling clusters. Both seem to aim at designs aiming at creating useful estimates of between/within group (strata, cluster) variation, and ...
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Long-tailed predictors for likelihood prediction

I have a set of long-tailed predictors and a binary target variable. The long-tailed predictors have also 0 values. The data-set is imbalanced with about 5% of positive class samples. I would like to ...
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what is the best sampling procedure to use? (stratified)

all, i am a little confused on what sampling method to use. i am doing a classification problem where i am trying to classify a person as having cancer or not. there is apparently huge variance in ...
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Main options on how to deal with imbalanced data

As far as I can tell, broadly speaking, there are three ways of dealing with binary imbalanced datasets: Option 1: Create k-fold Cross-Validation samples randomly (or even better create k-fold ...
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Should I maximize the number of respondent (total or per strata), or the similarity between the sample and the general population?

I want to conduct a market research survey. I want to interpret results for both the general population, which is very large, and also for age and household income subgroups. With the limited budget ...
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How to explain that leave-one-out cross-validation doesn't need stratification in the context of classification?

Stratification is usually defined as training data and testing data having the same distribution of class values. In leave-one-out cross-validation, each fold only has 1 instance. I know that it's ...
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Distribution of the grand mean (mean of means), and case of stratified sampling

I am having some trouble while estimating the distribution of the mean of means, according to the type of sampling. Basically, I sampled N=1000 sets of n=100 random numbers distributed as an uniform ...
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Post Stratification & Calibration? - Adding weight's - CALMAR

Let's suppose that I want to compare the average spending of both groups and I have two groups with 2 features in each one: Each client is unique and CityCode has only 2 groups CA and CB and AgeCode ...
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Asymptotic variance of Metropolis-Hastings estimates on a disjoint subdivision of the state space

I'm running the Metropolis-Hastings algorithm on a state space $(E,\mathcal E)$ which can be disjointly subdivided into regions $E_1,\ldots,E_k$, $k\in\mathbb N$ ($k\approx1e5$). On $E$, I have a ...
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Stratified Sampling vs. Hand picking a “random sample”, and using Chi-Squared as justification. Dichotomous outcome methods question

Suppose I would like to evaluate "the proportion of potato farms in Idaho that use pesticides". (dichotomous yes/no outcome variable). Now let's assume I know the exact number of potato farms in Idaho,...
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Sample stratification by dependent variable in linear regression analysis

I have a theoretical question that I would like some guidance on. Is it ok to stratify a sample population by the dependent variable? Does this bias regression results? For example, I'm doing an ...
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Choosing strata in stratified log-rank test

Let's say I would like to see which factors affect the survival of patients suffering from cancer. And let's assume I have two variables: chemo ($0$ - if a patient is not treated with chemotherapy or ...
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test of difference (equivalence) between pairs of hazard ratios derived from two models

I have two stratified cox proportional hazards regression models each restricted to data on men and women, respectively. Both models have the same covariates and the data on men and women come from ...
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Analysing stratified random sample

Let's say I am analysing an experimental design with treatment and control group which was based on random sampling design stratified on a number of covariates. When analysing the data I see two ...
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Stratified sampling to generate random numbers (eg. for Monte-Carlo applications)

I am using a Monte-Carlo method to compute a value of interest $y$ from some input parameters $x_{i}$, that I use to draw statistical sets from simple distribution laws. In my case, for a single Monte-...
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Percents of Random Samples

I have three sets $A,B,C$ of sizes $N_A=2508$, $N_B=36211$ and $N_C=2296$ respectively, containing binary values. I took 200 samples of each set to produce point estimates of the averages: $\hat p_A=0....
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Is leave-one-out cross validation (LOOCV) known to systematically overestimate error?

Let's assume that we want to build a regression model that needs to predict the temperature in a build. We start from a very simple model in which we assume that the temperature only depends on ...
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Should I use simple random sampling instead of stratified sampling when some strata have low counts?

I am designing a survey for which I would like to stratify "events" by state / province, creating $h=86$ strata for the particular dataset I'm working with. However, there are some strata with low ...
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On improving the sensitivity of controlled experiments by utilizing pre-experiment data (CUPED)

I’m currently reading “Improving the sensitivity of online controlled experiments by utilizing pre-experiment data” by Deng et al. and struggling to derive a few equations from the paper. I would ...
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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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Neyman allocation and proportional allocation

Suppose that a city has 90,000 dwelling units, of which 35.000 are houses, 45,000 are apartments, and 10,000 are condominiums. We want to estimate the overall proportion (p) of households in which ...
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Stratification of sample data is lowering my accuracy?

So I've got this trainingset, it has a bunch of stuff yada yada.. Main point is that there are two target variables that only occur once in the dataset. This means I can't stratify when sampling, I ...

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