Thanks Enrico for your pointer. I was not aware of them!

Thanks and regards,
_____________________________________________________

Arun Kumar Saha, FRM
QUANTITATIVE RISK AND HEDGE CONSULTING SPECIALIST
Visit me at: http://in.linkedin.com/in/ArunFRM
_____________________________________________________


On Sun, Nov 27, 2011 at 4:58 PM, Enrico Schumann <enricoschumann@yahoo.de>wrote:

>
>
> Am 27.11.2011 10:52, schrieb Arun.stat:
>
>> Hi Debashis, I dont think there is any direct implementation in R for
>> that.
>>
>
> Just for the record: see for example function 'nearPD' in package Matrix,
> or 'repairMatrix' in package NMOF.
>
>  What you need is to have a PD matrix for the underlying VCV matrix, which
>> is
>> not the case for yours. Do you have lot of missing data? May be you can
>> try
>> with constructing VCV matrix after considering pairwise variables and then
>> tweak the eigen value little bit to make your estimated VCV matrix PD.
>>
>> One algorithm for that may be like (if your estimated matrix is NND):
>>
>> Mod_VCV = Original_VCV + (Identity_Mat(n) - Original_VCV)*(Lambda/(1-**
>> Lambda)
>> + a_very_small_positive_number)
>>
>> Lambda is the smallest eigen value for you estimated VCV matrix.
>>
>> HTH,
>>
>> Thanks and regards,
>> ______________________________**_______________________
>>
>> Arun Kumar Saha, FRM
>> QUANTITATIVE RISK AND HEDGE CONSULTING SPECIALIST
>> Visit me at: http://in.linkedin.com/in/**ArunFRM<http://in.linkedin.com/in/ArunFRM>
>> ______________________________**_______________________
>>
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>> Sent from the Rmetrics mailing list archive at Nabble.com.
>>
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>>
> --
> Enrico Schumann
> Lucerne, Switzerland
> http://nmof.net/
>

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