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σ n > σ n + 1 =···= σ n + d ,then
X F X F
If
(1.49)
σ n + 1 also influences the difference in condition between the TLS and OLS prob-
lems and thus the difference in numerical accuracy of their respective solutions
in the presence of worst-case perturbations. σ n
2
n + 1 is a measure of how close
AX B is to the class of nongeneric TLS problems. Assuming that A 0 X B 0
is the corresponding unperturbed set, rank A 0 = n and the perturbations in A and
B have approximately the same size, the TLS solution is more accurate than
the OLS solution, provided that the ratio n σ
σ
0
n
)/σ n > 1, where σ
0
n
1 is the
( n + 1 ) th singular value of [ A 0 ; B 0 ]. The advantage of TLS is more remarkable
with increasing ratio: for example, when
+
1
+
σ n
B 0 F is large, or when
0, when
v n of A 0 associated with its smallest
X 0 becomes close to the singular vector
singular value.
1.9.1 About the Residuals
Proposition 30 Define the LS residua R as B AX and the TLS residual ˆ Ras
B A X; then
1 A A T A σ
1 I 1 X
R R = σ
2
n
2
n
(1.50)
+
+
R F R F
(1.51)
The TLS and LS residuals approach each other if:
1.
1 is small (slightly incompatible set).
2. B F is small (i.e., the TLS solution becomes close to the LS solution).
3. σ n σ n + 1 [i.e., A may not be (nearly) rank deficient].
4. B is close to the largest singular vectors of A .
σ
n
+
If σ n + 1 = 0 ,then R = R = 0 .
Proposition 31
1.10 STATISTICAL PROPERTIES AND VALIDITY
Under the assumption that all errors in the augmented matrix [ A ; B ] are row-
wise independently and identically distributed (i.i.d.) with zero mean and common
covariance matrix of the form σ
2
ν 0 , 0 known and positive definite (e.g., the
identity matrix), the TLS method offers the best estimate and is more accurate
than the LS solution in estimating the parameters of a model. The most suitable
model for the TLS concept is the errors-in-variables (EIV) model, which assumes
an unknown but exact linear relation ( zero residual problems) among the true
variables that can only be observed with errors.
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