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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20 views

Stratified Sampling from a population according to given weights

I have a population and i want to take a sample from it in order to examine a mean of a characteristic (say $p$ the probability of having an infringment).My population is 13996 and are divided into 7 ...
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27 views

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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181 views

Best way to construct a QQ-plot

I want to assess the normality of a dataset (which is log-normally distributed data transformed back to normal) using a Q-Q plot. I stumbled on the fact that there are many ways to build such a plot, ...
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22 views

Propensity Score Weighting in GAMLSS

in a project of mine i want to use a propensity score weighted gamlss model. However, the gamlss user guide states "In general using weights that are not frequencies is not recommended unless the ...
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1answer
115 views

How to estimate the (approximate) variance of the weighted mean?

Background: weighted mean In the context of survey statistics it so happens that a sample of respondents from a survey are fit some weights to adjust their answers to the general population. These ...
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1answer
36 views

An alternative sampling without replacement

Consider a set $X := \{x_1, \ldots, x_n\}$ with corresponding weights $p_1, \ldots, p_n$. Suppose we would like to draw $m < n$ distinct (i.e. unique) elements in a way that the probability of ...
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11 views

Comparing rates of different populations given as percentages instead of raw numbers

Here's an example of what I mean. 500 Consider a hypothetical game played by members of a population of unknown size. Group A is 13% of the whole population and scores 500 points in a game. Group B ...
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14 views

Handling class-imbalanced independent variables in binary classification

I am working on a binary classification model (in R, if that matters). I have done some research and reading on how to handle a class imbalance in the dependent variable, with different sampling ...
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22 views

Sampling with a variable number number of picks

Imagine we have N items and some weights w for each item, we first draw a random integer s (fom uniform) $s \sim int(U(1, N))$ and then we sample s items according to weights w (no replacement). ...
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179 views

How to calculate weighted average based on two variables: sample size and pollster grade?

Let's say I have a dataset with three columns with 5 rows and 3 columns. Each row represents a separate poll result of a single political party. The columns are poll prediction, sample size of the ...
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19 views

K-means++ for weighted clustering

I have implemented k-means for weighted points; that is, the final clusters take into account the fact that each input point is weighted. I wanted to initialize the clusters using k-means++, and I was ...
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12 views

"Weighted empirical distribution", terminology question

I have some weighted simulations of two variables. I would like to have an idea of how they are correlated. An option is to use a bivariate density estimate which allows the weights. Another option is ...
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22 views

How should one compute confidence intervals for means computed with inverse propensity weights (IPW)?

Inverse propensity weighing involves a machine learning model that takes features and outputs the predicted probability that this person is in the sample. Let $w_i$ be the inverse of the output for ...
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298 views

Questions about object function and loss function in weighted logistic regression

According to what i learned in machine learning, the loss function is derived by the Maximum likelihood estimation of training data. Taking logistic regression as an example: we got a train data set $\...
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1answer
50 views

How can I prove that two algorithms for weighted sampling without replacement are equivalent?

I have a table with N rows and n unique elements. Let j denote the row index and i denote the element. In the table below $N=9, n=3$. Let $w_i$ denote the count of element i. For example, $w_1=4, w_2=...
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70 views

Sample unique elements from an array containing repeated values

I have a table containing elements in $[1,c]$. The elements may be repeated in the table. I want to sample $m$ unique elements from this table. I can reduce this problem to weighted sampling without ...
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136 views

Neural Networks: How to set the weights for weighted sampling for semantic segmentation?

I'm currently trying to do semantic segmentation with a deep learning model on images. The dataset is highly imbalanced and i would like to try weighted sampling. I'm using pytorch and a dataloader ...
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18 views

Help with weighting sample according to population

I am a beginner with basic knowledge of statistics - just learning. I have a doubt regarding weighting survey sample distribution to population distribution. I have to create a weighting variable that ...
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2answers
1k views

Is there any reason to factor in sample weights when applying a scoring function to a test set?

It's my understanding that sample weights are used to ensure that each observation used to train a machine learning model are given a weight corresponding to its perceived importance/value to the ...
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1answer
83 views

Nested Uniform Distributions in Monte Carlo Integration

In terms of importance sampling for numerical Monte Carlo integration we can proceed as follows: \begin{align} \int_{\Omega} p(\mathbf{x}) d\mathbf{x} &= \int_{\Omega} p(\mathbf{x}) \frac{q(\...
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29 views

Estimating from Biased Sample

ORIGINAL: This is a rather convoluted problem, but I will try to explain it as clearly as I can. I have a discrete, known population with 1,000,000 possible values (call this set A), with roughly ...
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22 views

Calibrate Sample: What kind of data do I need to address non-respondents? [closed]

I want to weigh my sample to include non-respondents in my estimations. We have multiple factors which should be taken into account. So it's not only about weighing after gender for example. We have ...
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947 views

Correct use of the sample weights in a complex survey design for association analysis (Logit OR)

I've doubts about the correct use of sample weights in the NHANES survey, which uses a complex, multistage probability sampling design (1). I'm aware about the importance of the use of the sample ...
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472 views

How to compute confidence intervals from *weighted* samples?

Imagine we have a webserver, which serves a total of N static URLS. There are users visiting the URLs every day. At the end of each day, we have data like this: ...
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1answer
100 views

why sampling weights that I have range from 1, not 0?

I am looking at a dataset from Pew Research Center. Inside the dataset, different survey waves have their own weight variable with sampling weights. I thought in general it is supposed to range from 0 ...
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1answer
137 views

How to use derivatives of a function to better estimate its variance over the domain?

How to use derivatives of a function to better estimate its variance over the domain? I have a scalar smooth function $f(x)$ and a multivariate random variable $x$ with known distribution (e.g. ...
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27 views

Computing the Sample Size for the sum of Bernoulli RVs with different probabilites times a constant

I have the following statistic for which I need to figure out a sample size: $$S= \frac{1}{n}\sum_{i=1}^n \left(c_i+\sum_{j=1}^{100} b_{ij}X_i\right)$$ where $c_i$ and $b_{ij}$ are constants and $...
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Which is the right way to handle imbalanced data in a regression problem?

I'm working on a regression problem with imbalanced data, and I would like to know if I'm weighting the errors correctly. I'll try to illustrate the concept with a simple example. Imagine I'm ...
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1answer
18 views

Uncertainty-minimizing stratified sampling strategy

Suppose there is a school, and I want to know what proportion of students like the color red better than green, or vice versa (suppose there is no "other" option, just a binary variable). The school ...
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1answer
16 views

Resampling to get equal predictive power per observation

Cross posted from data science due to lack of response This is probably a thing I am just not searching for correctly, but essentially my idea is this: given some machine learning classification $C$ ...
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1k views

How to calculate importance weights for update step of an SIR (Sequential Importance Resampling) Particle filter?

I understand that one may use a particle filter to solve the filtering problem (estimating the hidden state of a system which can be described as a Hidden Markov Model). If I have a system where I ...
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414 views

Equivalence of svyglm and glm for simple random surveys

I have been exploring the use of the svyglm function in R's survey package to analyse surveys with both equal and unequal sampling probabilites. For an unequal ...
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1answer
276 views

Finding median without raw data?

I have only summary statistics for each state in the United States. I have the mean and median prices for each state and that’s it. How can I estimate an “overall” median price for the nation? I ...
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1answer
70 views

Application of Bayesian Averaging for Ranking

I have a sample with two metrics and one ratio per attribute. I am trying to rank the attributes based on the ratio and variable amounts and from my research I have found that most people find ...
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373 views

What is the Effect of Weighting observations when training a Classifier and how it can be combined with Subsampling?

My question is what is the effect of assigning weights to observations when training a Classifier such as a Logistic Regression model. The glm function documentation in R for example states: Non-...
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1answer
2k views

Equivalent to weighted random sample? [closed]

Let's say that you have a list of numbers and a weight for each number e.g. X = [(1, 2342), (2, 55), (3...] In the above example, 2342 and 55 are weights. Is weighted random sampling N items from ...
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233 views

Hypothesis testing on Weighted Poisson Binomial Distribution

Suppose I have $i$ coins, all of which are weighted to have a different probability $p$ of flipping heads. This results in $i$ Bernoulli distributions with different $p_i$. Cumulatively, this results ...
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1answer
63 views

Can someone point me towards research works relevant to Importance or Weighting Datapoints like SAW(Stepwise adaptation of weights) technique?

I am working on Fitness case importance for Symbolic Regression and found a Paper "Step-wise Adaptation of Weights for Symbolic Regression with Genetic Programming" which talks about weights of ...
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107 views

what is weight vector and bias in svm [duplicate]

I'm trying to understand the SVM algorithm but not able to understand what weight vector and bias is ? Could anyone explain it in laymen terms.
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1answer
4k views

SE of weighted mean

$X$ is a random variable with unknown distribution. A number of experiments are conducted to estimate $X$. Each experiment has a different reliability measure in estimating $X$. These $n$ experiments ...
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1answer
714 views

Survey sampling : normalised weights or not?

I work in epidemiology on a sample which is stratified, and 2 – level cluster (801 individuals). I use the "survey" package on R for data analysis. In the sample, the sum of weights is the size of ...
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1answer
711 views

How Do I Weight a Dataset to Match Needed Demographics in R?

If I have the attached file of 100 people with this dataset with the following demographics/regions: Region: Center: 12%, East: 62%, West: 26% Sex: Female: 82%, Male: 18% Party: D: 89%, R: 4%, O: 7% ...
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1answer
252 views

Ranking with weighting amount of data

I'll try to ask this question in a form of a hypothetical: I have 10 different advertising spaces and I want to rank them according to their conversion rate (conversions/views). BUT, I have an uneven ...
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116 views

Appropriate sample size for weighted sample

I have a population that is sampled such that each item has a different probability of being selected. That probability is separate and independent of the value of any given item. How do I determine ...
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1answer
103 views

Test fitness proportionate selection algorithm implementation

I implemented a couple of algorithms for fitness proportionate selection (roulette-wheel, alias method and roulette-wheel via stochastic acceptance) and now I want to write a test to ensure that ...
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1answer
690 views

"representative sampling" from a distribution [closed]

I'm drawing samples from a distribution to train a machine learning classifier (training it via mini-batches of 32 samples at a time). It's just a toy dataset, so I know that the samples are coming ...
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1answer
38 views

Evaluating quality of a sampler on a small subset of the entire sample space

Assume a multi-dimensional discrete sample space $X$, which is "large", e.g. millions of possible objects. Function $f: X \rightarrow (0;1]$ that assigns a "reward" to each object $x \in X$ (the ...
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64 views

Biased coin randomization with adjustments over time

Assume an experiment that was going to consider different randomization strategies to address different challenges. The experiment involves an experimental (active treatment) and control group (usual ...
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1answer
98 views

Power Analysis Weighted Data

Suppose an outcome depends on the intensity of a treatment intervention $\pi$, where $\pi \in [0,1]$. Given intensity of treatment $\pi$, the data generating process is $$Y_i = \beta_0 + \beta_1 \pi +...
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333 views

For classification w unbalanced datasets, is class-weighing the same as oversampling?

in unbalanced classification problems, I find myself using class_weigh = "auto" or similar parameters often, but I don't think I'm fully understanding what it's doing. I know that it's the industry ...