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Unformatted text preview: T x. The vector c ∈ Rn is the
model parameter, which we want to choose. We will use a least-squares criterion, i.e., choose c to
48 minimize K J=
k=1 y (k ) − c T x (k ) 2 . Here is the tricky part: some of the values of y (k) are censored; for these entries, we have only a
(given) lower bound. We will re-order the data so that y (1) , . . . , y (M ) are given (i.e., uncensored),
while y (M +1) , . . . , y (K ) are all censored, i.e., unknown, but larger than D, a given number. All the
values of x(k) are known.
(a) Explain how to ﬁnd c (the model parameter) and y (M +1) , . . . , y (K ) (the censored data values)
that minimize J .
(b) Carry out the method of part (a) on the data values in cens_fit_data.m. Report c, the value
of c found using this method.
Also ﬁnd cls , the least-squares estimate of c obtained by simply ignoring the censored data
samples, i.e., the least-squares estimate based on the data
(x(1) , y (1) ), . . . , (x(M ) , y (M ) ).
The data ﬁle contains c...
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This note was uploaded on 09/10/2013 for the course C 231 taught by Professor F.borrelli during the Fall '13 term at University of California, Berkeley.
- Fall '13
- The Aeneid