outaovii

Identification of outliers in a one-way analysis of variance model II (random effects).

Description

In a one-way analysis of variance random effects model one would expect that the measurements lie close together because the same quantity was measured and states that the class effects stem from a common source and therefore should not difer too much. In the random model some variation is allowed and the decision is assisted by the assumption of the distribution of the random class effects. But one can observe some type of 'outliers', that according to Barnett and Lewis (1994), it is an observation or subset of observations which appears to be inconsistent with the remainder of that set of data. In random effects model can be distinguished three types of outliers:
           1. Within the classes
           2. Within the random effects
           3. Respect to scale (objects occupy the same relative positions in one measurement space as they do in the other).
 
If the model is satisfied, these outliers are not likely to occur because of the light tails of the normal distribution and homoscedasticity, and it is considered to describe the ideal situation without outliers. Therefore, it is important to set up formal rules that identify these outliers. Wellmann and Gather (2003) provide rules and details of a robust procedure which involves the median-based estimators.

Here, we developed a MATLAB function that deals with such a fundamentals.

**NOTE: For an unbalanced design we are still working on in order to implement the procedure to detect the class scale-outliers. Please, keep on date of the file updatings.**

 Inputs:
      X - data matrix (column 1= data;column 2=class code).
  alpha - significance (default = 0.05).

 Outputs:
A complete summary of the identification of outliers:
   - within the i-th class.
   - within the random effects.
   - as a scale-outlier class.

• MATLAB release: MATLAB 6.5 (R13)

[zapp]

it seems the only data it can take is the vector from the example inside the function.... ;(

[zapp]

 

[Antonio Trujillo-Ortiz]

Hi zapp,

I think your rate is so rigourous, and twice (only one should be fine)...!!!

Recall that I'm not the author of the statistical procedure, but only of the Matlab function. So, I suggest you could contact the authors and statistically establish your point of views. Also it could be better you publish your deep statistical rejection.

Finally, are you sure what the procedure statistically does?

Yours,

Antonio Trujillo-Ortiz