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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40 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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18 views

Calibration of RF classifier: with sample_weight vs. without sample_weight

I am working with Random Forest binary classifier and use isotonic regression (using CalibratedClassifierCV from sklearn) for probability calibration. The question: assuming that RF classifier is ...
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1answer
31 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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17 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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14 views

Adding Post-stratified Weights to IRT 3PL Model

I am using IRT, 3 Parameter Logistic Model (3PL) for parameter estimation of my question parameters. As my data is biased I calculated Post-stratified weights using sample and population data. I want ...
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19 views

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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8 views

sample weights for time series classification

I am using time series of macro data (i.e. FX rates, commodity indices, sentiment indices, bond yields/spreads, and equity indices and change in their P/E estimates, etc) for predict market pullback. ...
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10 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
90 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
72 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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24 views

Understanding weighting class estimator

What I understand is that in this scenario, the data is partitioned into Z weighting classes on the basis of variables observed for respondents and nonrespondents. $n_z$ represents the sample size, $...
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15 views

Comparing Sample to Weighted Population Estimate

I'd like to compare an outcome (% of people who experience some event) in a sample we collected locally (N is about 1,000) to national (US) estimates. I have estimated frequencies/percentages, ...
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Stochastic/batch gradient descent (type SGD/ADAM) with weighted mean square error loss

Assume I assign uneven weights to losses of different examples, i.e. I set my SGD/ADAM to train a universal approximator f (e.g. a neural net) by minimization of a weighted mean square error: $ L = \...
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Compensating for bias in a location-based weighted average

I have a data set where each point comes from a specific geographic location. I want my users to see a weighted average of the data, so that the average is biased toward their location. The problem ...
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26 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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What is best way to sample from words that is representative of original distribution of words

Suppose I have a large distribution of words with their absolute counts in documents. As an example just take five words 1. Facebook 1000 2. Google 2000 3. Twitter 300 4. Quora 40 5. Reditt 60 If I ...
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53 views

How to do post-stratification weighting in sampled groups with low or zero cell populations?

Let's say you received data from a sample. You don't have information on the sample design, but you have population data so you want to employ post-stratification weights to better align the sample ...
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1answer
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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1answer
499 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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1answer
166 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
29 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
62 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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2answers
574 views

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
15 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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1answer
580 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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263 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
198 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
62 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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216 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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180 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
60 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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88 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
2k 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
518 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
604 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
183 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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0answers
112 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
85 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
601 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
34 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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55 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
85 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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332 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 ...
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655 views

Ranking based on weight for multiple variables and objectives

First of all, I know very little about statistics. I need help ranking a set of multiple solutions (about 138 solutions) based on three objectives. I work on the architectural design field, I analyzed ...
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66 views

What is the Big O of rejection sampling from large sets of weighted items (like billions of records)?

On average, how many "rejections" will I get before I get an acceptance (for large sets using weights)? This answer suggested O(log n)?
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1k views

How to generate a specific number of random binaries with probablities proportional to given values?

I have such a matrix in an excel sheet. It has 140 cells. I want to generate 20 binaries randomly. However, the probability of generating "1" in each cell is proportional to cell's value. Namely, ...
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1answer
1k views

Probability for selecting centroids - K-means++

K-means++ selects centroids one by one, where each point has the chance to become next centroid with probability proportional to distance to closest centroid already selected. I implemented it like ...