Questions tagged [weighted-sampling]
If you have survey data with weights, please use "survey-sampling" instead. If you need to draw Monte Carlo samples from a distribution that is intractable/inconvenient, and have to use a sampler from a simpler distribution that you would then correct with weights, please use "importance-sampling", "monte-carlo" and/or "simulation" instead.
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Weighing Data Issue
I am looking at e-cig prevalence within a city. I used surveys to collect data from residents, and I have a query around weighing data.
I have made the assumption, due to over and underrepresentation ...
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Is R's weighted sample without replacement function misleading?
Background
The 2023 article "Remarks on some misconceptions about unequal probability sampling without replacement" by Tillé suggests the sample function ...
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Is there any point upsampling a minority class if it is 40% of the dataset? [duplicate]
The minority class of my target variable is 40% of the dataset. Is there any point to upsampling them to 50%? or is upsampling only used when there is severe class imbalance?
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Is there any statistical advantage to using a deterministic sample size in unequal probability sampling with the Horvitz-Thompson estimator?
Say I'm sampling from a large population of size $N$ without replacement, and denote by $\pi_i$ the probability that unit $i$ is included in the sample, and $\pi_{ij}$ the probability that both $i$ ...
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Assign weights to examples in a highly imbalanced dataset
I have a highly imbalanced dataset and I'd like to train a simple ANN classifier on it. My model currently is a simple 2-layer feed-forward neural network with ReLU activation in between. After a few ...
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Stratified SRS vs. probability-proportional-to-size (PPS) sampling - what's the difference?
If my understanding is correct, the key difference is that:
In stratified SRS you intentionally draw $N_h$ samples from each of your $k$ strata ($h = 1...k$, $\sum_{1}^{k}{N_h} = N$) and are ...
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Upper bound for covariance of Hortvitz-Thompson Estimators
I need to bound on a covariance quantity that has come up in a sampling problem. $\widehat{Y}$ and $\widehat{T}$ are Horvitz-Thompson estimators of population totals, $Y=\sum_{i=1}^N y_i$ and $T=\sum_{...
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How to improve sample representativeness for longitudinal data collected via an online platform?
I am working with a longitudinal dataset exploring cognitive ageing (e.g., memory performance over time). Participants complete the study annually. Inclusion criteria for this study are 1) UK resident,...
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Is there relationship between propensity score based causal inference and sampling weights?
Consider observational study with single outcome $Y$, single covariate $X$ and treatment assignment variable $W$. Under unconfounded treatment assignment assumption, $E_{sp}[Y(1)]=E[\frac{Y_i^{obs}W_i}...
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Non-parametric bootstrap for 95%CI calculation in stratified sample in R
I am estimating the population mean of the 2023 value of cars from a stratified sample. The value of the cars is right skewed on visual inspection, and some basic diagnostics indicate normality ...
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What is the (Ratio estimator for the) covariance of two weighted means? [closed]
In a previous question I've asked How to estimate the (approximate) variance of the weighted mean?, specifically, how to prove the following formula:
$$
\widehat{\sigma_{\bar{y}_w}^2} = \frac{1}{(\sum{...
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Amplification effect of retweets on uncertainty
Consider you are scoring tweets for tone based on some sentiment analysis implementation. Each tweet has hypothetically a 90% chance of being correctly scored, while 10% get it wrong for whatever ...
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Density of sampled exponential data, with sampling weights proportional to x itself
Suppose $p(x) = \lambda e^{-\lambda x}$. However, our probability of observing a given sample of $x$ (denoted $z$) is further proportional to $x$ itself, i.e., $p(z\mid x) = \lambda e^{-\lambda x}$. ...
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Probability of drawing one element before another in weighted sampling without replacement
Setup:
The setup is weighted sampling without replacement. By which I mean:
You have a set of $n$ items, indexed by integers 1 through $n$, and the items have associated weights $\{w_1,\ldots,w_n\}$ ...
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Logistic regression for case-control studies
If I have designed a study where participants from 3 disease groups of fixed size were being sampled and suppose the three groups A, B and C are of sizes n_A=50, n_B=50 and n_C=100. Group A is a ...
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