Dear list,
I've searched the archives and tried some code, however would appreciate some
input - even a pointer in the direction of the correct function to use.
Given N samples each of which is measured for characteristics x1, x2, x3,... (m
6) where each characteristic is a roughly normally distributed numeric, but
with different center and scale.
Then the N samples are measured again for characteristics again as x1, x2,
x3,..., however the identity of the samples is unknown.
Is there a function which will assign the unique identities from the first
measurement to the second measurement?
I've tried scaling by using the pooled variance of each x1 (i.e. 2N values to
estimate the variance of the measure of characteristic x1, the characteristic
x2, etc.) to construct the normalized distance from one sample's second
measurement x1, x2, x3... to each of the first measurements and then pick the
minimum distance to assign an identity to the second measurement. Then loop
over all the second measurements to find the first measurement "closest" to it.
However I result with one sample ID from the first measurement being assigned
to multiple second measurements.
How could I minimize the matching between the second measurements and the first
with unique sample ID assignment?
Example:
measure height, weight, and blood pressure of 100 people with their names
recorded (scale and ruler both have some random unknown error)
measure the height, weight, and blood pressure of those 100 people again, but
you forgot to write down their names. (assume that the scale and ruler errors
have not changed since the first measurement)
How to assign the second set of measurements to the first?
Leif Kirschenbaum, Ph.D., PMP
Principal Reliability Engineer
Parts Engineering
Design Reliability
Product Reliability
SSL
3825 Fabian Way M/S H-21
Palo Alto, CA 94303
Tel: +1-650-852-6580
Facsimile: +1-650-852-7832
www.ssloral.com
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