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where z D y T x b T is
; X D H T I n T is
.m C n/ 1
.m C n/ n
© D Œ © o © b T
and
.m C n/ 1
is
with zero mean and variances matrix
R
0
D
0
B
The generalized LS solution for
is BLUE and is given by
™ D .
X T 1 X
/ 1 X T 1 z
(4.34)
see Talag r an d ( 1997 ). After some algebra this equation equals ( 4.11 ). Thus
z D X ™ D H T x a
x a T D X
X T 1 X
/ 1 X T 1 z
.
and by ( 4.5 ) the influence matrix becomes
S yy S yb
S by S bb
R 1 HAH T R 1 HA
B 1 AH T
z D @
S zz D @ z
@
z D
D
B 1 A
@
where S yy D @ Hx a
@ y
I S yb D @ x a
@ y I S by D @ Hx a
D @ x a
@ x b
@ x b I S bb
. Note that S yy
D S as defined
in ( 4.4 ).
Generalized LS regression is different from ordinary LS because the influence
matrix is not symmetric anymore. For idempotence, using ( 4.33 )iteasytoshow
that S zz S zz D S zz :
Finally,
D B 1 A D I n H T R 1 HA
S bb
hence,
H T R 1 HA
tr
.
S bb / D n tr
.
/ D n tr
.
S yy /
it follows that
S bb / D n
The trace of the influence matrix is still equal to the parameter's dimension.
.
S zz / D tr
.
S yy / C tr
.
tr
References
Bauer P, Buizza R, Cardinali C, Thepaut J-l (2011) Impact of singular vector based satellite data
thinning on NWP. Q J R Meteorol Soc 137:286-302
Bormann N, Saarinen S, Kelly G, Thepaut J-N (2003) The spatial structure of observation errors
in atmospheric motion vectors from geostationary satellite data. Mon Wea Rev 131:706-718
Cardinali C (2009) Monitoring the forecast impact on the short-range forecast. Q J R Meteorol Soc
135:239-250
 
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