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Questions tagged [weights]

"Weights" may refer to: (1) observation weights that come from sample surveys -- consider tagging "survey-sampling"; (2) Monte Carlo sample weights that arise when sampling from intractable distributions -- consider tagging "weighted-sampling"; (3) variable weights in statistical or machine learning models such as regression, factor analysis, or learning networks -- consider tagging with that specific model. Other odd uses of weights go here.

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1answer
16 views

Tensorflow choice of values of variables after training [on hold]

I am trying to build a neural network, that is able to perform a linear regression. After for example 1000 epochs, I encountered the situation, where the smallest loss-value was not the last loss-...
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0answers
24 views

Effective Sample Size for Weighted Samples

I have an MCMC sampler with weighted samples and I want to compute effective sample size at every step to determine sample degeneracy. I am using the following formula: $ESS = \frac{(\sum_{i=1}^N{w_i}...
2
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1answer
35 views

Save Machine Learning Model progress for later [closed]

another dumb question, but how do you save the progress an ML model has made and start from that point later? Its kind of a vague question, but this is an example of what I am talking about: Say, ...
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0answers
15 views

Why does weighting increase the standard error of an estimate of a proportion?

Imagine I am running a survey with a non-probability sample. The population is 5,000 people, and my sample is about 100 people. There are two binary variables, $x_1$ and $x_2$, that predict the binary ...
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0answers
10 views

Wrong weights learned when training RBM

I'm training my RBM network and on epoch #4 I have such a filters representation (my weights matrix) But on the next iteration (fifth epoch) something went wrong and my filters became like this What ...
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1answer
21 views

How to compare multiple weight vectors of different size?

I am facing a statistical problem that I am not sure whether it's solvable. Simply put, I am given multiple weight vectors and here are two examples: $w_1 = [.2, .3, .4, .1]$ for items A, B, C, D, ...
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1answer
27 views

What are the theoretical/practical reasons to use normal distribution to initialize the weights in Neural Networks?

I'm aware that there are many different practices of initializing the weights when training a neural network. It seems traditionally standard normal distribution is the first choice. Most articles I ...
2
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1answer
54 views

for a multinomial treatment and binary outcome, what is more appropriate, ATC or ATE?

I need help to choose between ATC and ATE for my analysis with multinomial treatment and binary outcome. In the example below taken from here, it seems that ATT does not sound well for multinomial ...
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1answer
44 views

how to calculate manually propensity score weights for multinomial treatments where one of them is baseline

I want to get intuition into the calculation of propensity scores (PS) and inverse probability of treatment weights (IPTW) for a multinomial treatment using multinomial regression. One of the ...
0
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1answer
16 views

Is there a way to check the full learnt function by Neural Network, not only the weights? [closed]

The training is mostly learning about the wights.But what about the full function learnt by NN? In typical deep learning framework, is there a way to example the function learnt? For example: ...
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0answers
13 views

Weighted covariance with different reliability weights?

Suppose observables $X$ and $Y$ possess different reliability weights $w_{x,i}$ and $w_{y,i}$ for the possible elements $x_i\in X$ and $y_i\in Y$ respectively. Considering a sequence of consecutive ...
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0answers
20 views

Should weights be applied in generated quantities block in stan?

I want to do predictions via generated quantities block in stan. I have two questions: Should the weights be applied again in the generated quantities block in addition to the likelihood in the ...
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0answers
18 views

“Strongest held belief” tests - data analysis problem

The data comes from an online test where each question only has a yes/no answer. There's no pattern or theme, and each question has an equal random chance of being asked. There are hundreds of ...
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3answers
86 views

What are weights in a binary glm and how to calculate them?

I have a dataset that includes four variables. Three of them are factors and one is constant. My response variable contains (0,1) so my glm is about logistic regression. My question is, how do I know ...
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0answers
16 views

Weighted Empirical Cumulative Distribution Function (CFD): order values before or after weighting? [before]

My input data are some observations of a continuous variable, let's say the number of kilometers of motorbikes. I did a survey and I collected some observations. Somehow, I computed weights for all ...
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1answer
56 views

Why does weighted bootstrap have awful coverage even in toy example?

I'm interested in using the weighted bootstrap to correct for selection bias with a known form. I simulated a very simple example where the underlying data, $X$, are $N(0,1)$ and we are calculating a ...
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0answers
17 views

Sample weights in covariance matrix estimator

I am using Logistic Regression with count representation. So, for any feature-tuple, I have few 0's(negative class) and few 1's. I duplicate each row, one for target 0 and other for target 1, And I ...
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0answers
87 views

How to use data_utils.WeightedRandomSampler and still be able shuffle training data in Pytorch?

I am working on the multi-label classification task in Pytorch and I have imbalanced data in my model, therefore I use data_utils.WeightedRandomSampler method ...
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0answers
13 views

Regarding frequency and inverse probability weights

Deploying inverse probability weighting on a 3-group Setup, I examined for outlier activity, aswell deployed in a survey bin. Regression model. These estimates of ...
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0answers
25 views

Error variance estimation with weights

I am using data which include a (sample-)weight for each observation, i.e. the data is from a survey that has weights to make the sample representative for the US-population. I perform OLS to get some ...
1
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1answer
66 views

Expected value of weighted random variable

I have a statistic I'm investigating that depends on estimates from a correlation matrix. The statistic is: $$ T = \sum_{i=1}^{m} d_{i} w_{i} $$ where $d_{i}\sim Bern(\pi)$ and is independent of $w_{i}...
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0answers
21 views

Question on a particular step in the Paule-Mandel 'Consensus Values and Weighting Factors' paper

I am preparing to use the Paule-Mandel method for determining the weighting factors in a meta-analysis. The paper I'm reading is the original one available here. I believe only those who are ...
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0answers
4 views

Magnitude of Weights vs Features for Output Layer

I have a question regarding the magnitude of weights and features for the output layer of an RNN. The RNN outputs a hidden layer matrix h of dimensions (64, M, N) which is then reshaped into a (64, M*...
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1answer
44 views

Given unnormalized weights in logs, how do I compute normalized weights in logs?

Setting Given a set of positive weights $\{w_i\}_{i=1}^n$, I can normalize them by computing $$W_i = \frac{w_i}{\sum_{j=1}^nw_j}\quad \forall i=1,...,n.$$ Easy enough. But for numerical reasons, it ...
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0answers
49 views

Quota sampling in R

I was provided surveys with quotas set at the provincial, gender, and employment status levels. I was wondering if it is possible to use survey package in ...
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1answer
9 views

Weight of MLP is larger than 1

I noticed when training MLP that weights of neurons can be larger than 1. Would this have negative effects on the outcome of the network? If yes, how to mitigate this problem?
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1answer
34 views

Combination of hierarchial time series forecasts with different methods - setting weights

I am trying to forecast the the number of orders for different products of a product group. I have the time series for each product. One of the problems is that some/most time series are intermittent ...
0
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1answer
33 views

When and how to use withSampW in the function get.weights of the Package twang?

I am using Twang R package in my analysis but I have doubt as to when and how should I use or not use the function get.weights. The package documentation here and ...
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0answers
82 views

Why does this simple weighted quantile differ from Hmisc::wtd.quantile? Which method is to be preferred?

It just struck me today that we should be able to use the weights method of stats::density.default to roll our own simple ...
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1answer
47 views

back propagation in neurons with zero weight and some specific conditions

I have read a lot of articles to understand what is happening behind the scene in backpropagation like Ive gone through this and many other like that. I think I understand how the backpropagation ...
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0answers
50 views

Effective sample size of a weighted sample

I am trying to understand the meaning of the effective sample size when we have weights for the sample. I have an intuitive understanding of why effective sample size can be less than the raw sample ...
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2answers
67 views

Is ratio of ranks a well-studied statistic?

I have two lists of recommendations: that is, two different algorithms have assigned ranks to the same list of objects. I would like to know if they're similar. I specifically care more about ...
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0answers
21 views

How to test for difference in weighted proportion difference? (and average weighted proportion differences)

Suppose I have a survey where respondents rate different questions as positive (1), negative (-1), or neutral (0), and their responses are given a different weight based on which job they have and ...
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0answers
16 views

Benefit of scaling survey design weights

In the european social survey the design weights are scaled to the sample size (see quote and source): As with the design weights also the post-stratification weights are scaled to the sample size, ...
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0answers
8 views

Assigning weights for binary endpoint analysis, using survival analysis

This problem concerns the evaluation of predictors which could potentially be used to decide on which patients to perform colonoscopy. Study design: Analysis of administrative data - patients ...
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0answers
38 views

Using multinomial logistic regression to make predictions [closed]

I have run a multinomial logistic model in SAS with 5 independant variables and I need to use the results from this model to make forecasts of use of care. I have used the predicted probabilities from ...
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0answers
13 views

Weighting data with uneven groups

I've seen that you can account for uneven amounts of respondents within different groups, but every example has the same amount starting. How do you handle if you have an uneven starting number per ...
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0answers
23 views

Shouldn't we consider larger standard errors for effect measures or outcomes that are converted in meta-analysis?

There are methods to convert effect measures in meta-analysis (pdf). There are also methods to convert outcomes; at least, I am aware of the conversion described in Furukawa et al. (2005) from ...
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0answers
77 views

Simulating by hand the predictions of a complex mixed model with beta distribution from glmmTMB for diagnostic

I am conducting a GLMM for a meta-analysis using the beta distribution with the package glmmTMB. My response variable is a vector of correlations (No exact 0 or 1), but Fisher’s transformation fails ...
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2answers
208 views

Regression as a way to determine variable importance

At my work, we employ a nearest neighbor algorithm to classify records. Part of this process, of course, includes determining which features to use as auxiliary information in the algorithm. Also, ...
2
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1answer
52 views

Mixing of liquids where concentration follows normal distribution

We have several liquids where the concentration of a certain element follows a normal distribution, and we take a weighted combination of the elements. The concentrations are: $$C_i \sim \text{IID N}...
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2answers
320 views

How do sample weights work in classification models?

What does it mean to provide weights to each sample in a classification algorithm? How does a classification algorithm (eg. Logistic regression, SVM) use weights to give more emphasis to certain ...
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1answer
28 views

Potential bias of effect sizes due to ceiling (or floor) effects when weighting effect sizes by their variance for meta-analysis

Regarding meta-analysis, I am wondering whether there is a potential bias due to the ceiling effects (or floor effects) when weighting effect sizes by their sampling error variances. In case of ...
2
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2answers
138 views

How to find the weight of the weighted least squares regression analysis?

As the title, I am having trouble to the find weight at the weighted least squares estimation. I found that some people use weights like wts <- 1/fitted(lm(abs(residuals(regmodel.1)) ~ x))^2 or ...
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0answers
21 views

Weights of ratio of weighted data

Suppose I have the two datasets $x_i$ and $y_i$ with weights $w_{x,i}$ and $w_{y,i}$, respectively ($1 \le i \le N$). The weighted means are for example: $$\bar x = \frac{\sum_i x_i w_{x,i}}{\sum_i ...
2
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2answers
100 views

Which weighting to use for regression analyses at different levels of aggregation

I run a study with subjects in 400 groups of heterogeneous sizes ranging from 2 to 20 individuals. I have outcome data at the group level and at the individual level. Treatment was randomly assigned ...
2
votes
2answers
66 views

Reweighting features in PAM/K-means clustering

As stated in Hennig et al. 2016 Handbook of cluster analysis: If for subject matter reasons some variables are more important than others regardless of the within-variable variation, one could ...
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0answers
18 views

Formulating the Netwon Raphson

If this is the dataset under consideration and ...
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0answers
110 views

Regression with varying weights within fixed effect units

I'm running a regression where within the fixed effect groups, the weights are not constant. However, since there are many fixed effects, I would like to use an estimation method as implemented for ...
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0answers
80 views

Adding weights to forest plot with subgroups

I am creating a forest plot with subgroups (roughly following this example). Now I want each subgroups to be weighted as if I would plot them individually in a separate forest plot, and the weights ...