Concept of model validation and outlier detection in JAG3D (Software)

E_merlet, Sonntag, 16. April 2017, 01:37 (vor 11 Tagen) @ Micha

They manipulate the probability value in a specific way. Please take a look to e.g.:

  • Aydin, Demirel (2004), Computation of Baarda’s lower bound of the non-centrality parameter
  • Šidák (1967), Rectangular Confidence Regions for the Means of Multivariate Normal Distributions.

Hello Micha, thank you for the reply.
I would like to read these books but they are not in free access in internet.

I'm really sorry to disturb you but I read a lot of paper about the least squares method and Fischer-distribution, Student-t-distribution, chi-squared distribution? test of hypothesis but I'm completely lost.

Here is the thing as I understood it.
Jag3D computes the compensation with the least squares method. Then it computes a global test.
This test tells us if there is/are outlier(s) in the observation(s) but it doesn't tell us where. Therefore Jag3D computes individual tests for each observations.
If the value of the individual test is near from 0, it means the observation is not an outlier and if the value of the test is big, it means the observation is an outlier. Right?

Is it the right order of computation?

Does the expression Tprio~Fm,∞ means that the test follows a Fischer-distribution?
Why does Jag3D compute an individual test a-priori and an individual test a-posteriori?

And if we already know where the outliers are thanks to individual tests, why do we use B-method or Sidak correction?
What do you mean with "They manipulate the probability value in a specific way"

I read so many papers but it sounds like chinese and I'm close to give up but I don't want it.
So, once again sorry for disturbing you.

Best regards, Etienne

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