Questions tagged [weighted-regression]

Weighted least squares regression is a generalization of OLS regression used when different data points have different importance, or "weights". See also [weighted-data].

Filter by
Sorted by
Tagged with
0
votes
0answers
53 views

Weighted Least Squares vs Monte Carlo comparison

This is a copy of a question originally posted on stackoverflow I have an experimental dataset of the following values (y, x1, x2, w), where y is the measured quantity, x1 and x2 are the two ...
0
votes
0answers
8 views

Weighted least squares using poisson regression link-log example in R

I'm studying about “weighted least squares” using poisson regression example. And I could got it for poisson link “identity” as following. it would be well because of the same one to minimize (or ...
3
votes
1answer
89 views

Goodness of Fit ot Least Squares with known measurement uncertainty

We want to estimate $\beta$ for $$ y = x\beta + \epsilon $$ where $y$ and $x$ are $n\times 1$ vector and $\epsilon$ is not i.i.d, but $\epsilon \sim N(0, \sigma^2\Omega)$, where $\Omega$ and $W$ are $...
0
votes
0answers
7 views

Calculate Variance Inflation Factors for WLS (using python package?)

Is there an easy extension of VIFs (variance inflation factors) to WLS regression, hopefully easily available in Python? I have an application where I am optimizing the operation a system, modeled as ...
0
votes
0answers
21 views

Interpreting regression coefficients in weighted linear regression

I have a dataset where I'm doing Multiple Linear Regression. Examining the residuals vs fitted plot, it was seen to exhibits heteroskedasticity. As an antidote, I am doing Weighed Least Squares ...
0
votes
0answers
9 views

Weighted zero inflated negative binomial regression

Before encountering the ZINB model, my choice of model was the standard NBR model with the R function glm.nb. My dependent variable is non-negative integer in ...
1
vote
0answers
75 views

Prove variance of locally weighted regression increases with degree

I am interested in proving the following fact for locally weighted polynomial regression from The Elements of Statistical Learning by Hastie et. al. It can be shown that $||l(x_0)||$ increases with ...
0
votes
0answers
23 views

How to use survey weights correctly in logistic regression with R?

I'm currently working with data from the European Social Survey (ESS). With it come several weights varaibles to use for analyses. I tried to use them the following way conducting a logistic ...
1
vote
1answer
39 views

Ratio Estimator as WLS

Apologies in advance for whatever rules this post breaks. I'm looking at a problem where we're currently using a ratio estimator for a certain survey. $$r = \dfrac{\sum_i{y_i}}{\sum_i{x_i}}$$ This ...
0
votes
0answers
11 views

What is the likelihood ratio test for the significance of a term in a Cox's Porportional Hazards model with a weighted dataset?

(Please see reproducible example below.) When fitting a Cox proportional hazards model in R, I'm trying to compute the significance of some terms in order to drop them in case they are irrelevant, ...
0
votes
0answers
21 views

How to apply different weights on independant variables in lmer?

I am trying to build a mixed-effect model to predict an outcome based on three independant variables. Below is the line of code I wrote so far. ...
1
vote
0answers
48 views

Combining estimates from multivariate multiple regression using MICE in R?

A similar question was posted last year, but it didn't get any love, so!: I'm trying to calculate a pooled estimate after fitting a multivariate regression in multiply imputed data (having used MICE ...
0
votes
0answers
29 views

Best method for rescaling weights [duplicate]

I'm investigating the effect of overhead cover (tree canopy) on the proportion of birds (in relation to mammals) scavenging on carcasses left out in nature. For this study I placed 35 carcasses in ...
0
votes
0answers
15 views

Calculating weights for weighted regression

I want to weight my regression by the square root of a which I'm doing by applying a weighting function to my x and y variables before running the regression. I'm a bit confused, however, but whether ...
2
votes
0answers
39 views

Dynamic Linear Model (DLM) vs Weighted Linear Regression

In Applied Bayesian Forecasting and Time Series Analysis (1994, page 14), they state: The passage of time erodes the value of information - sales figures from six months ago are potentially less ...
2
votes
0answers
13 views

imbalanced regression problem + lower bound prediction + custom error weighting

I'm looking for a simple approach (e.g. defining a new target label / sample weights and then using some off-the-shelf regressor with some standard objective) for the following problem: I want to ...
1
vote
1answer
38 views

appropriate use of prior weight in glm

I am modelling the effects of several variables on the number of scallops caught. The variables are: Stratum (categorical, 23 levels), Vessel (categorical, 3 levels) , Density of scallops (continuous)...
1
vote
1answer
39 views

Comparing mixed models with weighted variance

I'm performing some linear mixed models for a psychological experiment. I'm not a statistician so my knowledge is limited. The basic idea is that: I have an experiment in which I model my response ...
1
vote
1answer
62 views
0
votes
0answers
24 views

Weighted OLS predictions

I am experimenting with cross validation to determine a cap for weights in weighted OLS (because the weights become large). I first estimate $\hat{\beta}_{trn}$ using the training data. Do I (A) ...
4
votes
1answer
66 views

Non-Linear regression and variance misspecification

Given a non-linear regression model for cross-section data $$y_i = f(x_i,\theta_0) + \epsilon_i,$$ where it is assumed that $\mathbb E[y_i\lvert x_i] = f(x_i,\theta_0)$, I understand that it is a ...
2
votes
0answers
62 views

Weights formula for Weighted Linear Regression [closed]

I am working with Weighted Linear Regression. I have read that I need to calculate the weights $W_i$ to perform this regression. My question is; Which formula should be used for calculating the ...
0
votes
2answers
67 views

What does “weighted logistic regression” mean?

What does "weighted logistic regression" mean? I came across this term "weighted logistic regression"in this paper. I have read the paper a lot of times throughly. But I still can't get the idea of ...
0
votes
0answers
43 views

weighted linear regression and outliers

I am fitting a regression model with weights because without weights I have heterocedasticity. Suposse $\epsilon \sim N(0,x_i \sigma^2)$ Then check the weights through aux model Model $Y=X \beta + \...
1
vote
1answer
26 views

Variance Estimation for Least Squares with Probability Weights

I'm running a simulation study and finding that the nominal SEs of the estimated coefficients when using weights in lm in R are an underestimate of the simulation SE. I have confirmed that $\hat{\beta}...
3
votes
1answer
250 views

How does R compute r.squared for weighted least squares?

I used R for fitting a linear model using weighted least squares (due to heteroskedastic errors): $\boldsymbol{y} = \boldsymbol{X}\boldsymbol{\beta} + \boldsymbol{\varepsilon}$, where $E(\boldsymbol{\...
0
votes
0answers
12 views

Measure of association adjusting for varied number of observations per person (binary outcome and predictor)

I would like to ask a question about how to alter a model to deal with varied numbers of cases per person (1 vs. 2), or, if I should deal with it some other way, some suggestion around that. I would ...
0
votes
0answers
42 views

How to generate a weighted regression from repeated experiments in R & using a weights matrix with lm? Is this possible?

I was told in another question that glm cannot do multivariate analysis, so I'm hoping the weights matrix idea isn't doomed through lm, too. I've also spoken with 4 classmates about this (grad ...
0
votes
0answers
24 views

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 = \...
0
votes
0answers
27 views

Determining weights for fitting non-uniformly spaced measured data (v2)

Let's pretend I have some data to which I want to fit a line. If the data are uniformly spaced along the x-axis, I get the following: If the data are not uniformly spaced, I get a different fit line:...
0
votes
0answers
28 views

Determining weights for fitting non-uniformly spaced measured data

I have a system of generally known behavior, and some non-uniform measurements of that behavior (let's say without measurement error). Now I want to fit a simple function to a subset of the ...
0
votes
0answers
55 views

Selection of sample_weight for gradient boosted regression

I'm looking for any information on how the sample_weight parameter is typically selected for gradient boosted regression tree's - i.e. implementations such as ...
1
vote
1answer
45 views

Convergence of weighted regression solution to the solution of L1 regression

I have read in this paper that the weighted regression solution converges to the solution of L1 regression, for weights, $$W_i=1/|y_i-\hat y_i|$$ I worked this out but unfortunately, I lost. Could ...
1
vote
1answer
144 views

Confidence interval for mean of m predictions

I have a gradient boosted regression tree model (catboost). $y = F(X) + \varepsilon$ I need to compute the following - specifically the 2nd equation below, the first is trivial. $\frac{1}{m}\sum \...
1
vote
1answer
249 views

Poisson regression with offset vs poisson regression with weight [duplicate]

I would like to know the linear expression of weight and offset in terms of poisson regression in glm. for instance for offset ...
0
votes
0answers
35 views

Linear regression: equivalence of forms of the minimum variance, affine unbiased estimator

Background Consider the linear regression model: $$y = X\beta + e\\E[e] = 0 \quad E[ee^T] = V$$ It is well known that the minimum variance affine unbiased estimator (MVAUE) of $\beta$ exists if and ...
0
votes
1answer
23 views

How do I use weighted least squares in a matrix formulation of a multiple regression analysis?

If the coefficients of OLS multiple regression can be determined by: β = (X'X)^−1 * X'Y, then what formula/matrices would be used for WLS?
1
vote
0answers
51 views

Formulas for multivariate meta-regression

everyone! I'm digging in meta-regression and doing hand calculations using WLS to get better understanding of the topic. I'm fine with calculations for univariative model (that is, 1 covariate is ...
0
votes
0answers
12 views

analytic weights when the outcome is a standard deviation

I have a research question evaluating the longitudinal divergence of a profit index across businesses within a market. The index is continuous. I plan to use a market-level random-effects model where ...
1
vote
1answer
93 views

Weighting in Likert Scale results

I have referred to similar questions on this topic, but the problem presented then was not quite in line with my research, so I have created a new question. My research is the following: I have ...
0
votes
1answer
48 views

Does local linear regression include a weighting Kernel?

I am applying a Regression Discontinuity Design (RDD) to estimate the effect of a policy change. In RDD I can apply the parametric approach (polynomial regression) and the non-parametric approach (...
1
vote
0answers
27 views

How to perform regression with different error variances? [duplicate]

I have two series of measurements values, first series is X and second is Y. I need to model Y as a function of X, where I know the method that was used to measure X is two times better then the ...
2
votes
0answers
193 views

How to decide what variables to weight in a WLS regression

My question concerns the weighting procedure used when using weighted least squares and multiple variables. I'll start by explaining my own limited understanding of how to conduct a weighted ...
0
votes
0answers
68 views

How can analytical weights be used together with sampling weights?

I run a linear probability model (LPM) on survey data, which contains sampling weights. Say the predicted probabilities from the OLS regression are $\hat p_i$. Heteroskedasticity would make me ...
0
votes
1answer
194 views

Out-of-sample predictions with prediction intervals using WLS in statsmodels library

I am using WLS in statsmodels to perform weighted least squares. The weights parameter is ...
0
votes
0answers
27 views

How to calculate weights for a weighted ordinal regression, with > 2 possible outcomes (unbalanced)?

I'm trying to perform an ordered logistic regression with the response variable being a certain 'level' of achievement (level 1 = highest level, level 2 is one below, until level 10 - the lowest level)...
0
votes
1answer
80 views

Weighted least squares versus ordinary

Is it ever better to use ordinary least squares (OLS) over weighted least squares (WLS)? If a model is fit well by OLS, will I get worse results if I use WLS instead? Obviously, OLS is faster, but ...
0
votes
0answers
46 views

Appropriate weighting factor in weighted least squares regression

I'd like to construct a regression model with expenditure on a certain public service (a continuous variable in £s) as the predictor, and productivity (represented as a continuous variable on an index)...
0
votes
0answers
116 views

Restricted Weighted Linear Regression in R

I have to follwing issue. I would like to run a linear regression imposing a constraint on the weighted coefficients. Let me construct an example: Consider the following cross-sectional regression $...
1
vote
1answer
318 views

Why do stabilized IPW weights give the same estimates and SEs as unstabilized weights?

In Cole & Hernán (2008), the authors mention that using stabilized weights can decrease the variance of the effect estimate. Regular inverse probability weights use the probability of being in the ...

1
2 3 4 5 6