November 2012
Intermediate to advanced
794 pages
22h 9m
English
book
Probability, Random Variables, and Random Processes: Theory and Signal Processing Applications
by John J. Shynk
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9.20 LEAST-SQUARES ESTIMATION
Assume that information about the parameter vector 
is obtained via the following measurement model:
(9.309) 
where Y is a random variable, a is a known vector, and V is an unobservable additive noise random variable. This is a generalization of the simpler model Y = X+V discussed previously where X is a random variable. If 
happens to be a random vector, its distribution will not be taken into account in least-squares (LS) estimation. Assume there are N iid samples 
such that
where 
and
Usually, the number of samples far exceeds the number of parameters, that is, 
so that a is a tall narrow matrix. Let be an estimate of the measurements based ...
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