Hi,

This is a practical question and I am sure there are many statisticians can
give me a hand.

I have 500 time series data (500 rows), each row contains 100 intervals,
i.e., on each row, I have X1, X2, ..... X100.  I am trying to reduce the
dimension of this input because the data at the end of each row does not
have significant meaning to the project I am doing.

I used cubic splines on ea. row -- ns(row, df = 10) , and decided to reduce
the dimension to 10.  Then I generated the new feature by X_new = W*X, where
W is obtained from ns(row, df = 10).  Now everything seemed perfect and I
was able to fit the logistic regression on the new inputs (500 rows, ea. row
has only 10 X's).  After I computed the coefficients for this reduced model,
I want to map these coefficients back to the original problem. i.e., the new
coefficients has 10 betas, but I actually want 100 betas that I can use to
predict the response from the raw inputs.   I donot know how to do this.

Any comments are highly appreciated.

thanks.

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