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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.

45
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3answers
48k views

Understanding stratified cross-validation

What is the difference between stratified cross-validation and cross-validation? Wikipedia says: In stratified k-fold cross-validation, the folds are selected so that the mean response value is ...
25
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2answers
14k views

Why use stratified cross validation? Why does this not damage variance related benefit?

I've been told that is beneficial to use stratified cross validation especially when response classes are unbalanced. If one purpose of cross-validation is to help account for the randomness of our ...
2
votes
3answers
197 views

Derive househould weights from a uniformly distributed person sample

The Swiss Public-Use Sample of the national census is a 5% sample drawn from the entire census survey. According to the documentation, the persons are sampled uniformly without replacement. Persons ...
9
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3answers
2k views

Multilevel model vs. separate models for each level

What are the advantages and disadvantages of running separate models vs. multilevel modeling? More particularly, suppose a study examined patients nested within doctors' practices nested within ...
7
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2answers
4k views

Remove duplicates from training set for classification

Let us say I have a bunch of rows for a classification problem: $$X_1, ... X_N, Y$$ Where $X_1, ..., X_N$ are the features/predictors and $Y$ is the class the row’s feature combination belongs to. ...
7
votes
1answer
1k views

Is stratified meta-analysis more or less objective than meta-regression?

Reviewer asked me why I use meta-regression as a way how to deal with heterogeneity among effect sizes instead of conducting stratified meta-analysis. I tried to google "stratified meta-analysis" and ...
3
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2answers
2k views

Using post-stratification weights in R survey package

I am analyzing a dataset that has a variable for post-stratification weights. As this is a complex survey, the plan is to use the R survey package. I have been ...
7
votes
1answer
2k views

Machine learning with weighted / complex survey data

I have worked a lot with various nationally representative data. These data sources have a complex survey design, so the analysis requires the specification of stratification and weight variables. ...
5
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1answer
5k views

Bootstrapping stratified sample that is weighted to population - reweighting during the bootstrap?

I'm using R to provide bootstrap (percentile and t methods) of estimated population totals, using data from a complex survey. It is a stratified survey of tourists expenditure that is weighted to ...
4
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1answer
118 views

Downsides of stratified randomization in experimental design

When sample sizes are too small to trust that the usual asymptotics will guarantee good balance across experimental groups on known confounders, a common approach is stratified or blocked ...
4
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0answers
68 views

an alternative allocation method for small RCTs?

stratification and minimization are two randomization options to achieve balance in terms of covariates or baselines in small RCTs. in recent years, researchers seem to have favoured minimization, ...
1
vote
1answer
85 views

How to combine two sets of samples from an unnormalised distribution?

Imagine that I have an unnormalised distribution $P$ with density function $p(x)$ for $x \in \mathbb{R}^d$. $P$ has two well-separated modes and there are two sets of i.i.d. samples with the size $n$ ...
2
votes
2answers
380 views

How to partition a training-set when I have a big class imbalance?

In my actual data class A has 90%, class B has 9% and class C has 1% (numbers are made up for sake of simplicity). Now I want to prepare a training set for my classifier (I plan to use Vowpal Wabbit). ...
1
vote
1answer
197 views

Analysis of complex sample

The company where I work did a survey with a complex sample. Originally, the sample was a 2-step stratified, but due to some problems, we lost one of them (so, let's consider that the sample is ...