| Re: regression optimization [message #71391] |
Thu, 17 June 2010 01:36  |
Klemen
Messages: 80
Registered: July 2009
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Member |
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On Jun 17, 10:01 am, Dave Poreh <d.po...@gmail.com> wrote:
> On Jun 17, 12:35 am, Klemen <klemen.zak...@gmail.com> wrote:
>
>
>
>> Hello all,
>
>> I have a variable to be explained with 12 attributes (at the moment
>> all of them being arrays of 300 by 100, but in the future it can be
>> even larger than 1000 by 1000 array). I would like to preform a moving
>> window analysis. For each window I want to estimate the optimal
>> multiple linear regression parameters.
>
>> Optimal means that it is not necessary that all of 12 variables are
>> used (some of them are correlated). So I want to get out an equation
>> that has 2,3,4... parameters and provide the best statistics.
>
>> The problem is also that not all the values within a moving window can
>> be used - some pixels contain no data. Defining NaN for these values
>> and using the code similar to the one written by Bringfried Stecklum
>> ( http://groups.google.com/group/comp.lang.idl-pvwave/browse_t hread/
>> thread/17613c70b78f1ac4/6891d260db6c7c93?
>> lnk=gst&q=regression#6891d260db6c7c93), I can test which of attributes
>> might be significant for the regression.
>
>> The question is, how to proceed. From those attributes that I know
>> that they are correlated among each other, I want to use just the one
>> that explains the most variability. I can somehow imagine to select
>> the final 2-4 attributes that should be used for multiple regression
>> without using any FOR loops. But how do I do the final step -
>> estimation of multiple regression parameters without using any loops?
>> Any idea?
>
>> Thank you!
>> Klemen
>
> I think the problem that you mentioned is a principle component
> analysis problem (PCI). For each state you have to apply this analysis
> and then select the first, second or what ever variables you need to
> justify the total variance. David has a very good example on it
> (http://www.dfanning.com/code_tips/pca.html).
> Cheers
> Dave
Hi Dave, thank you for your suggestion. I will think of it. I have
never used PCA before. I am just afraid that computing for each moving
window a 13 by 13 covariance matrix and its eigenvectors is also not
really to easy to write without any for loops.
Klemen
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