From: maneesh deshpande <dmaneesh_at_hotmail.com>

Date: Wed 28 Dec 2005 - 13:54:21 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Dec 28 13:59:30 2005

Date: Wed 28 Dec 2005 - 13:54:21 EST

Hi,

I have the following problem which I would appreciate some help on.

A variable y is to be modelled as a function of a set of variables
Vector(x).

The twist is that there is another variable z in the problem with the
property that y(i) <= z(i).

So the data set is divided into three categories

I. y(i) = z(i)

II. Both y(i) and z(i) are known and y(i) < z(i)
III. y(i) is not known but z(i) is known ( But y(i) is guaranteed to be <
z(i) )

The data in categories I + II can be satisfactorily modelled via a OLS
regression of the form:

y ~ Vec(x)

The question is how to incorporate the information contained in the category
III data?

The category II data can be used to construct a model for y given z. Indeed
log(z(i)-y(i))

is reasonably normal and so the following is a decent approximation:
y(i) = z(i) + A*exp( N(0,1) )

This model can be improved by including Vec(x).

After this I am not sure how to proceed :-( :-(

Thanks in advance,

Maneesh

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Dec 28 13:59:30 2005

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