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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15 views

survey weights on a datasets divided based on east or west

My dataset is divided based on east/ west German states.Total east states have data from 5 different cities and each of them varies in number of observation and same for west.DO I need to apply ...
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19 views

Logistic Regression, converting to weights [closed]

I have logistic model, which has coefficients between 0 and 1. However, the sum of all coefficients doesn't lead to being to one. I understand we can't normalize the logistic coefficients and use them....
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Assign more importance to recent records during training

I have a large dataset with information from the last year. I have to build a classification model in order to predict if a customer will buy a product or not (binary classification). Since in the ...
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Weights in multi-level models

I really hope that this is the right place to ask my question and that it hasn't been asked yet - otherwise please let me know. I am currently running a multi-level regression model with a cross-level ...
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25 views

Euclidean distance from zero

I am trying to create my own weights for relative work task importance, or weight. For every task, I have a value of importance, ...
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40 views

distance between two points (x,y) weighted by location (x)

a new on algebra. I am trying to create an indicator of the distance between two points (x,y) from a (0,1) scale, but I want to create a weight that reduces such distances as the point x is closer to ...
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10 views

Assigning weights to features for clustering

I want to run a K means clustering algorithm (using scipy/scikit) on a 1mn records dataset. I have a list of 5 features. Each of these features have a predetermined weight assigned to them: F1, 25% F2,...
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1answer
64 views

Non-normality in linear mixed models/GLMM

I have some data of time-depth profiles of whales. I want to model how the maximum depth of each dive (deepest point reached during a dive) changes between two dive types, foraging (if the whale feeds)...
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1answer
35 views

Meaning of Gaussian mixture weights?

other posts with similar title do not actually ask what's in their title, so I ask here: What is the meaning of the weights in the Gaussian Mixture Model (GMM)? Does the GMM weight more heavily to ...
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14 views

Problems with weighted logistic regression

Hope that you can help me with this one, it has been bugging me for the last couple of hours and I'm almost giving up on this one. I'm trying to fit a logistic regression on an unbalanced dataset. I ...
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1answer
26 views

In a meta-analysis, can you back calculate estimated group proportions from risk difference?

In a meta-analysis of the risk difference, does it make sense to apply the weights & back calculate the expected proportions in each group? For example, if you had the following: ...
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15 views

Matching with or without sample weights

I have data for a sample of firms indicating whether firms are treated, some covariates and a firm performance indicator that functions as dependent variable. The sample is weighted to assure it is as ...
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2answers
42 views

R and SPSS giving different weighted results

Edited again: I think that I figured out what was happening. It appears that SPSS rounds weighted counts to the nearest whole number, and then bases any subsequent statistics off of that. My sample ...
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How to define a weight function for response time?

I have a repeated measurement over time and the variable of interest is user compliance. However, since users use different questionnaire length, the duration of response time is also important. I ...
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Why is breaking symmetry important, when initializating the weights of a Neural Network? [duplicate]

In the beginning of the training process of a Neural Network, it's parameters, for example the weights in a Fully Connected Layer, have to initialized. There is a wide variety of schemes, how you can ...
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With a survey sample that includes probability weights, is taking the mean without using weights a biased/inconsistent estimator?

If you are estimating the population mean. or would it be sampling bias if one were to take an unweighted mean?
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What are the equations/methods to get some weighted score?

I have set of users and the topics they discuss as follows. ...
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1answer
34 views

Number of parameters in an LSTM cell

I am not sure how to calculate my LSTM weights based on this link and my Keras programing as below: ...
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7 views

Weighting Value Of Time Series Event Based On Subsequent Events

I am doing analysis on time series data and weighting the value of an event based on the outcome. More specifically, this is possession based basketball data. Let's say my data looks like this: <...
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15 views

Are probability weights “relative”, i.e. can they be rescaled and work in the same way?

I am using survey data from different countries, and I pool them together for part of the analysis. Now, weights puzzle me for two reasons: 1) some of them are below 0 (i.e. the probability of being ...
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1answer
23 views

Determining index weights

I am currently creating a multiple variable index and tried using Principal Components Analysis to determine the weight of each variable. Specifically I'm using the ...
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Correct way of giving weights using linear mixed-effects models in R

I want to understand how well a device (Device 1) estimate real activity (measured by Device 2) under different conditions: ...
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1answer
18 views

lmer gives -Inf for logLik when weighting is included

I have been trying to improve the residual distributions of a dataset with exponential decay data, using weights. The data is decaying leaf mass, where we knew the initial masses in each sample, which ...
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44 views

Can I adjust the relative influence of observations in a mixed model in nlme or lme4?

Is there an equivalent of lm's "weights" argument for nlme or lme4 ? Here's an example to illustrate my question in case it isn't clear enough: I have data taken from 3-5 trees at 3 sites. From each ...
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1answer
58 views

Estimate weights of individuals based on partially observed data

I can observe the sales $y$ in a set of stores $i$ for some products $j$. I want to estimate the share or the weight of each store. My main issue is that I don't observe all the sales data $y_{ij}$ ...
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41 views

Parameters count in GRU layer keras

I have this model structure and want to know the formula for calculating the parameters count in the GRU layer. I did not find that in the docs. ...
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15 views

Player ranking dependent on number of played games

I have a df of players and their penalty points. It looks something like this: ...
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1answer
74 views

Bayesian linear regression KL divergence

$$y_i \sim N(w_0 + w_1x_i, \sigma^2_j)$$ $$\mathbf{w} \sim N(0,\alpha^2 I) $$ Data is $D$, posterior distribution $p(\mathbf{w}|D)$ is approximated according to mean-field approximation $$p(\mathbf{w}|...
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11 views

How to weight additional data and keep initial results constant?

I am analyzing some panel data with a varied number of households per week. To adjust for this variation I created a weighting that deflates the contribution of weeks with more than the average number ...
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1answer
17 views

Calculate Sampling Weights

I have sampled 300 respondents, stratified by zones and have conducted a survey, prior to carrying a census (ideally would have been the other way round). I would like to compute sampling weights to ...
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36 views

Can gradient descent work iteratively instead of simultaneously?

When multivariate gradient descent is updating weight of single feature, why doesn't it project this new weight when updating weights of next features? Let's say we have this example of gradient ...
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70 views

What actually happens when we model a frequency instead of count (POISSON GLM)

First of all, I am using R. I know that we can model a frequency-response variable with a poisson regression, if we remember to weight it, so that the variance doesn't get affected by it. I am not ...
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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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1answer
83 views

Why does the first forward pass in a neural-network (NN) classification model computes to zero for all classes before final activation?

Suppose weights of NN are Gaussian spread initialization then forward pass for all the inputs will evaluate to zero which computes to 0.69 ($-\log_{e}0.5 \approx 0.69 $, since sigmoid (0) = 0.5) ...
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1answer
54 views

Kernelized perceptron algorithm weights update

I'm asked to find the maximum margin decision surface separating positive from negative samples by inspection. The positive examples are (1,1) and (-1,-1), the negative ones are (1,-1) and (-1,1). The ...
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1answer
43 views

Why the weight vector is a linear combination of the inputs and the outputs in the Perceptron

I was studying Support Vector Machines and I've got stuck with this relation regarding the weight vector of the hyperplane. $w=\sum\limits_{i\in I}^{} y_i x_i$ For reference, I'm studying from the ...
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38 views

Logit model marginal effects with survey data

I would like to estimate marginal effects with a logit model: How much is a person likely to work ("work") depending on the categorical variable "tier". I transformed "tier" into a factor variable. My ...
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PCA principal() r, where are the weights for the linear combination of weighted variables?

I want to identify the weight (w1, w2, w3, w4) that each of my variables (Y1, Y2, Y3, Y4) have on my components (C). Equation (1) : C = w1(Y1) + w2(Y2) + w3(Y3) + w4(Y4) Because I want to apply an ...
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11 views

Rescaling weights based on data-subsets of different analyses, is it statistically allowed?

I am running an analysis in R on the effect of canopy cover (OverheadCover) and the number of carcasses placed on the same location (...
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16 views

Stratified Sampling: use and interpret Instance Weight for EDA and modelling

Context I am working on a data mining project (EDA + Predictive Modelling) using the US census income data, which consists of training and test instances obtained using stratified sampling over the ...
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26 views

Latent time-series with multiple observations per time point

I have a latent time-series $\psi_t$ where for each time point $t$ I have multiple observations $y_t$, generated from a distribution dependent on $\psi_t$. The number of observations $n_t$ some time ...
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38 views

Difference between with and without “weight” option of the same data on logistic regression in R

I still keep checking from my previous question here. The next, I tried the case of proportion(=yes/yes+no), using previous best answer. Yes, I got it. But, I couldn’t understand the case without “...
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1answer
51 views

How should I consider the signs of the beta weights in a composite?

I have some biomarkers ($X_1, \ldots, X_5$) and I want to model an outcome ($Y$) using these biomarkers. The biomarkers are correlated. So I decided to use a ridge regression to stabilize the ...
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1answer
79 views

Glorot/ Xavier Init: for sigmoid and tanh?

My question is about Xavier Glorot Init. The assumptions that they make are that they approximate the activation function linearly, that this function has f'(0) = 1 and that we set the bias to 0, as ...
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17 views

Weightings using exponents

I've got this piece of work to do where we assign weightings to different variable to achieve a score: var A = 60% var B = 40% var C = 20% var D = 5% The score was calculated as: $(A^{0.6} x B^{...
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26 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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19 views

Multiply coefficients with different weights [closed]

I have two models with different weights (0.97, 0.11). Table of models and coefficients: Currently, I compute the final prediction using: $Prediction_{Final} = (Prediction_{Model1} * Weight_{Model1})...
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1answer
80 views

Complex survey design

I am currently analyzing a dataset resulting from a complex survey design. Individuals have been selected from a three-stage cluster sampling design with two strates (that have been combined to result ...
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113 views

Inverse probability of treatment weights and linear mixed effects models

I am encountering a problem when using inverse probability of treatment weights with linear mixed-effects models for a difference-in-differences analysis. I have longitudinal data on participants. I ...
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10 views

Help in learning feature weights

I need help with a problem. I have n features. With these features I can create a vector of size n. Given x vectors (where each vector is of size n) and a final score of the x vectors, what is the ...

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