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7
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0answers
181 views

Biased estimator for regression achieving better results than unbiased one in Error In Variables Model

I am working on some syntatic data for Error In Variable model for some research. Currently I have a single independent variable, and I am assuming I know the variance for the true value of the ...
3
votes
1answer
79 views

Intuitive meaning of error-in-variables

I understand the explanation of the example of error-in-variables used wikipedia. What I do not understand is how could we explain intuitively the error-in-variables problem? One way would be to say ...
0
votes
0answers
55 views

Troubles reporting transformed variables for log and sqrt into a general equation

Good morning everybody, I see CrossValidated has really high level of questions and answer; I am just a student so I hope this question is not too basic... Suggestion of further readings available ...
1
vote
1answer
151 views

How to do regression with error in variables and known correlations among the errors?

After the very satisfying answer to: How to do regression with known correlations among the errors? I take the question to my next point of interest: What can you do when you have a regression with ...
4
votes
0answers
192 views

Regression model with heteroskedasticity in both variables

I've been learning (lurking) from this site for a while and I finally have a question I haven't seen answered yet. I'm doing a flight test and trying to fit the resulting data to linear line. From a ...
0
votes
0answers
144 views

Binary regression with error-in-variables

I try to predict the binary, dependent variable $Y$ using a single predictor $X$. $X$ contains some amount of error, due to measurement problems. I would like to come up with a model for $X$, e.g. ...
6
votes
1answer
350 views

When submitting analyses to FDA how does using R affect issues of software validation?

I have worked on clinical trials in FDA submissions for many years. I use SAS almost exclusively. Recently I discussed a consulting job I had to bootstrap a Deming regression. Bootstrapping is much ...