Questions tagged [errors-in-variables]
Errors in variables are measurement errors which increase the estimation variance (error in the dependent variable) or bias the regression coefficients towards zero (error in the independent variables).
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theilslopes upper and lower bounds
How are the lower and upper bands computed in scipy.stats.theilslopes?
https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.theilslopes.html
I looked at the main reference
P.K. Sen, “...
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What is a good introduction to errors-in-variables models?
I'm aware of this resource https://en.wikipedia.org/wiki/Errors-in-variables_models, but I don't put a lot of faith into wikipedia articles on stats, so I'm looking for some reliable references on the ...
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Is reduced major axis regression a special case of total least squares?
Edit: It seems the answer to my first question is that the website has a typo. $\lambda = V_{y}/V_{x}$, NOT $\lambda = V_{x}/V_{y}$. But I'm still stumped on the second question about why it cannot be ...
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What is the Likelihood Formula for an Error in Variable Model?
I am comparing different models ability to explain my joint observation of (X,Y) with AIC for which I need the likelihood.
How can I calculate the likelihood of (X,Y) for the error in variable model ...
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Errors-in-variables regression with sample size weighting?
Very belated follow-up to a previous question:
I have some pretty simple linear models predicting a rate (continuous response var) from certain features of the distribution of some measured value. The ...
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A lot of variables in a Quadratic Discriminant Analysis
I'm trying to make a Quadratic Discriminant Analysis in R, but appears the follow mistake: "some group is too small for 'qda'". I was reading about it and I concluded that I have more ...
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Error-in-Variables regression p-value?
I ran an EIV model in r and I was wondering if there is something else besides the R-adjusted to see if the fit of the model is good?
I noticed that eivreg function ...
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Calculate the average of absolute values of a measurement with a measurement error
I have a few parameters; each is measured imprecisely with a known but unique random
measurement error. We can assume that the error is normally distributed, with mean 0 and known variance (different ...
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What is the best linear regression method when the errors in the variables x and y are unknown?
I have pairs of observations $(X_i,Y_i)$ with errors in both variables and I need to find the line that best fits the data. I have found some methods, but it is essential to know the standard ...
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Hypothesis testing using samples with different measurement errors/intervals
Are there generalizations of common hypothesis tests (e.g. t-test, mann-whitney) that can take into account different confidences in the sample measurements?
For example, if I have two sets of ...
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When calculating the statistical power of a t-test, do I need to consider the uncertainty of the single values?
I have a question regarding calculating the power for a statistical test that includes data which are estimated by a model (means they have an uncertainty):
I want to find out if two piles of stones ...
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Linear Regression but the Variables have errors
I have received this confusing task:
You have two variables 𝑥 and 𝑦, where y is a response variable which can be written as an explicit linear function of 𝑥. However, the technique used for ...
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What does Deming regression estimate?
Least squares regression estimates conditional means.
Least absolute regression estimates conditional medians.
Quantile regressions estimate conditional quantiles (a special case of which is the ...
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What is the name of this regression model?
I am wondering how I can map this problem to something known.
Let us start with a standard linear regression framework, and suppose we want to reconstruct an observed signal $y$ from single known ...
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How to test for correlated errors in regression
I understand that one assumption that must hold for regression is for there to be no correlation in the error structure.
Put another way:
The residuals should be impossible to predict above chance.
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