Hi all,

I have run a ridge regression on a data set 'final' as follows:

reg=lm.ridge(final$l~final$lag1+final$lag2+final$g+final$u,
lambda=seq(0,10,0.01))

Then I enter :

select(reg)   and it returns: modified HKB estimator is 19.3409
                                       modified L-W estimator is 36.18617
                                       smallest value of GCV  at 10

I think it means that it is advisable to use the results of regression
corresponding to lambda= 10;
so the next thing I do is:

best <- which.min(reg$GCV)
coef(reg)[best,]

which yields:

                             final$lag1    final$lag2       final$g
    final$u
 3.147255e-04  1.802505e-01 -4.461005e-02 -1.728046e-09 -5.154932e-04



Now,  by changing my data set(final), I repeat the process 100 times and
obtain 100 such vectors which I store as 100 rows in a 100X5 matrix:

matrix[i,]=coef(reg)[best,] (i varying from 1 to 100)


Now my final estimates for the beta's are:

Beta_0=median(matrix[,1])
Beta_1=median(matrix[,2])
Beta_2=median(matrix[,3])
Beta_3=median(matrix[,4])
Beta_4=median(matrix[,5])

I want to find the p-values of each of the estimated beta's.
I am confused how to extract these p values in R (may be we need to go back
to the reg= lm.ridge model corresponding to each final beta estimate, but I
am not sure how to do this through code)


Kindly tell me if any further details are needed.
Thanks for your help.

Regards,
Preetam

-- 
Preetam Pal
(+91)-9432212774
M-Stat 2nd Year,                                             Room No. N-114
Statistics Division,                                           C.V.Raman
Hall
Indian Statistical Institute,                                 B.H.O.S.
Kolkata.

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