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tatis
tical
m
eth
o
d
s
w
er
e
p
r
i
m
ar
il
y
m
ea
n
t
to
m
ea
s
u
r
e
th
e
s
tr
e
n
g
th
o
f
ev
id
en
ce
f
o
r
d
r
a
w
in
g
an
ac
c
u
r
ate
d
ec
is
io
n
in
h
y
p
o
t
h
esi
s
test
i
n
g
b
ased
o
n
a
s
am
p
le.
T
h
e
u
s
e
o
f
th
e
s
e
s
t
atis
tical
m
eth
o
d
s
i
n
s
p
ec
tr
u
m
s
e
n
s
i
n
g
is
i
n
v
e
s
ti
g
at
ed
in
p
r
ev
io
u
s
s
t
u
d
ies
b
u
t
t
h
e
s
tu
d
ies
d
o
n
o
t
f
o
cu
s
o
n
r
ec
ei
v
er
d
iv
er
s
it
y
u
s
i
n
g
m
u
ltip
le
an
te
n
n
as a
t t
h
e
C
R
d
ev
ice.
I
n
th
i
s
p
ap
er
t
w
o
b
en
ch
m
ar
k
s
co
in
ed
as
p
-
Val
u
e
a
n
d
Min
i
m
u
m
B
a
y
es
f
ac
to
r
(
MB
F)
ar
e
u
s
ed
i
n
d
ec
is
io
n
m
ak
i
n
g
i
n
h
y
p
o
th
e
s
i
s
test
i
n
g
.
T
h
e
f
ir
s
t
m
e
tr
ic,
p
-
Valu
e
i
s
v
ie
w
ed
as
a
n
i
n
d
ex
o
f
th
e
“stre
n
g
t
h
o
f
ev
id
en
ce
”
a
g
ai
n
s
t
H
0
,
w
it
h
s
m
all
p
in
d
icati
n
g
a
n
u
n
lik
e
l
y
h
y
p
o
t
h
esi
s
[
1
2
]
an
d
i
s
w
id
e
l
y
u
s
ed
i
n
m
ed
ica
l
r
esear
ch
a
n
d
d
ec
is
io
n
-
m
a
k
in
g
.
U
s
i
n
g
th
e
p
-
V
a
lu
e
th
e
co
m
p
atib
ilit
y
o
f
t
h
e
d
ata
w
it
h
t
h
e
n
u
ll
h
y
p
o
t
h
esi
s
i
s
m
ea
s
u
r
ed
.
T
h
e
s
ec
o
n
d
m
etr
ic
is
th
e
B
ay
e
s
f
ac
to
r
an
d
is
o
f
ten
r
ef
er
r
ed
to
as
th
e
“
s
tr
en
g
t
h
o
f
ev
id
en
ce
”
o
r
“w
ei
g
h
t
o
f
e
v
id
en
ce
”.
T
h
e
Min
i
m
u
m
B
a
y
e
s
f
ac
to
r
p
r
o
v
i
d
es
th
e
s
m
alle
s
t
a
m
o
u
n
t
o
f
e
v
id
en
ce
th
a
t
ca
n
b
e
s
tated
f
o
r
th
e
n
u
ll
h
y
p
o
t
h
esi
s
.
T
h
e
s
o
u
n
d
th
eo
r
etica
l
f
o
u
n
d
a
tio
n
o
f
th
e
MB
F
a
n
d
its
i
n
ter
p
r
etatio
n
allo
w
s
its
u
s
a
g
e
i
n
b
o
th
in
f
er
e
n
ce
a
n
d
d
ec
is
io
n
m
a
k
in
g
.
T
h
ey
h
a
v
e
s
tr
aig
h
t
f
o
r
w
ar
d
i
n
ter
p
r
etatio
n
as
t
h
e
s
tr
en
g
t
h
o
f
th
e
e
v
id
en
ce
in
f
a
v
o
r
o
f
H
1
r
elativ
e
to
H
0
[
1
3
-
1
6
]
.
T
h
is
w
o
r
k
is
t
h
e
f
ir
s
t
o
f
its
k
i
n
d
i
n
w
h
ic
h
t
h
e
u
s
e
o
f
p
-
v
alu
e
s
a
n
d
Min
i
m
u
m
B
a
y
e
s
f
ac
to
r
s
ar
e
p
r
o
p
o
s
ed
in
t
h
e
co
n
tex
t
o
f
s
p
ec
tr
u
m
s
en
s
i
n
g
.
T
h
e
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
w
s
:
Sectio
n
2
g
i
v
es
t
h
e
o
v
er
v
ie
w
o
f
b
li
n
d
s
e
n
s
i
n
g
s
ch
e
m
e
s
f
o
r
p
r
i
m
ar
y
u
s
er
d
etec
tio
n
,
Sect
io
n
3
d
is
cu
s
s
es t
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
,
Sectio
n
4
d
is
cu
s
s
e
s
t
h
e
r
esu
lts
an
d
Sectio
n
5
co
n
clu
d
es t
h
e
p
ap
er
.
2.
SYST
E
M
M
O
DE
L
AND
P
R
O
B
L
E
M
F
O
R
M
UL
AT
I
O
N
C
o
n
s
id
er
th
e
s
ce
n
ar
io
o
f
Sin
g
le
I
n
p
u
t
Mu
ltip
le
Ou
tp
u
t
(
SIM
O)
s
y
s
te
m
w
i
th
o
n
e
tr
an
s
m
it
an
te
n
n
a
an
d
m
u
lt
ip
le
r
ec
eiv
er
a
n
te
n
n
a
s
.
Ass
u
m
e
t
h
at
ea
c
h
C
R
co
n
t
ain
s
M
a
n
te
n
n
a
s
.
T
h
e
M
d
iv
e
r
s
it
y
b
r
a
n
ch
e
s
ar
e
ass
u
m
ed
to
b
e
s
u
f
f
icie
n
tl
y
f
ar
f
r
o
m
ea
ch
o
th
er
.
Hen
ce
t
h
is
s
tu
d
y
ta
k
es
f
u
l
l
ad
v
an
ta
g
e
o
f
t
h
is
as
s
u
m
p
tio
n
th
a
t
th
e
r
ec
ei
v
ed
s
i
g
n
als
ar
e
s
tat
is
ticall
y
i
n
d
ep
en
d
en
t
a
n
d
th
e
co
r
r
elatio
n
a
m
o
n
g
t
h
e
m
i
s
co
n
s
id
er
ed
to
b
e
n
eg
l
ig
ib
le.
C
o
r
r
esp
o
n
d
in
g
to
t
h
e
s
i
g
n
al
r
ec
eiv
ed
i
n
th
e
i
th
an
ten
n
a
o
f
t
h
e
C
R
d
ev
ice
th
e
h
y
p
o
th
eses
H
0
an
d
H
1
ar
e
d
ef
in
ed
as
H
0
:
x
i
[
k
]
=v
i
[
k
]
H
1
:
x
i
[
k
]
=h
s
[
k
]
+v
i
[
k
]
(
1
)
w
h
er
e,
h
is
th
e
am
p
li
tu
d
e
g
ain
o
f
th
e
ch
a
n
n
el,
i
is
th
e
an
ten
n
a
in
d
ex
(
i
=1
,
2
,
.
.
M
)
a
t
ea
ch
C
R
,
s
[
k
]
is
th
e
tr
an
s
m
itted
s
i
g
n
a
l b
y
P
U
an
d
v
i
[
k
]
is
th
e
A
W
GN
n
o
i
s
e
co
m
p
o
n
en
t.
2
.
1
.
E
x
i
s
t
ing
bli
nd
s
e
ns
i
ng
m
et
ho
ds
2
.
1
.
1
.
Sq
ua
re
la
w
det
ec
t
o
r
E
n
er
g
y
d
etec
to
r
(
E
D)
o
r
Sq
u
ar
e
la
w
d
etec
to
r
i
s
t
h
e
m
o
s
t
co
m
m
o
n
l
y
u
s
ed
m
e
th
o
d
f
o
r
h
y
p
o
t
h
esi
s
test
i
n
g
i
n
a
C
R
en
v
ir
o
n
m
e
n
t.
E
ac
h
in
d
iv
id
u
al
b
r
an
ch
at
t
h
e
r
ec
eiv
er
is
p
r
o
v
id
ed
w
it
h
an
en
er
g
y
d
etec
to
r
to
p
r
o
v
id
e
th
e
i
n
s
tan
ta
n
eo
u
s
i
n
d
iv
id
u
al
b
r
an
c
h
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y
m
ea
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m
en
t
s
.
T
h
e
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y
o
f
t
h
e
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s
ig
n
al
at
t
h
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th
b
r
an
ch
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Y
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a
n
d
N
is
t
h
e
s
a
m
p
l
e
s
ize.
T
h
e
d
ec
is
io
n
s
tatic
Y
i
is
co
m
p
ar
ed
ag
ai
n
s
t a
f
i
x
ed
th
r
e
s
h
o
ld
λ.
=
∑
|
[
]
|
2
=
1
(
2
)
T
h
e
s
i
m
p
le
h
y
p
o
t
h
es
is
tes
tin
g
p
r
o
b
lem
is
f
o
r
m
u
lated
in
E
q
u
a
tio
n
(
3
)
as
1
>
<
0
λ
(
3
)
T
h
e
o
p
e
r
a
t
i
o
n
i
n
E
q
u
a
t
i
o
n
(
2
)
i
s
e
x
e
c
u
t
e
d
u
s
i
n
g
a
s
q
u
a
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e
l
aw
d
e
v
i
c
e
p
r
o
v
i
d
e
d
a
t
e
a
c
h
d
i
v
e
r
s
i
t
y
b
r
a
n
c
h
o
f
t
h
e
C
R
r
e
c
e
i
v
e
r
.
T
h
e
f
o
l
l
o
w
i
n
g
c
o
n
v
e
n
t
i
o
n
a
l
s
q
u
a
r
e
l
a
w
c
o
m
b
i
n
i
n
g
t
e
c
h
n
i
q
u
e
s
a
r
e
u
s
e
d
t
o
f
o
r
m
a
b
e
t
t
e
r
e
s
t
im
a
t
e
o
f
t
h
e
p
r
i
m
a
r
y
u
s
e
r
s
i
g
n
a
l
[
7
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
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n
g
,
Vo
l.
9
,
No
.
4
,
A
u
g
u
s
t 2
0
1
9
:
2
9
1
0
-
2917
2912
a.
S
qu
a
r
e
-
l
a
w
s
e
l
ec
t
i
o
n
T
h
e
en
er
g
y
v
ec
to
r
s
f
r
o
m
M
d
iv
er
s
it
y
b
r
an
c
h
es,
Y
1
,
Y
2,
·
·
·
,
Y
M
a
r
e
u
s
ed
i
n
S
L
S.
T
h
e
b
r
an
ch
w
it
h
th
e
h
i
g
h
est e
n
er
g
y
is
c
h
o
s
e
n
.
T
h
e
test
s
tatis
t
ic
is
g
iv
e
n
as
=
(
1
,
2
,
…
.
.
)
(
4
)
b.
S
qu
a
r
e
la
w
co
m
bin
i
ng
T
h
e
e
n
e
r
g
y
v
e
c
t
o
r
s
f
r
o
m
M
d
iv
e
r
s
i
ty
b
r
an
ch
e
s
,
Y
1
,
Y
2,
·
·
·
,
Y
M
a
r
e
g
a
th
e
r
e
d
an
d
c
o
m
b
in
e
d
i
n
SL
C
t
o
m
ak
e
a
c
o
m
b
in
e
d
d
e
ci
s
i
o
n
.
T
h
e
t
e
s
t
s
ta
t
is
ti
c
is
a
s
=
∑
=
1
(
5
)
2
.
1
.
2
.
G
o
o
dn
es
s
o
f
f
it
t
ests ba
s
ed
s
ens
ing
An
o
th
er
b
li
n
d
s
e
n
s
i
n
g
m
et
h
o
d
is
th
e
g
o
o
d
n
ess
o
f
f
it
tes
t
s
(
Go
FT
)
.
T
h
ese
test
s
ar
e
b
l
in
d
n
o
n
-
p
ar
am
etr
ic
h
y
p
o
th
e
s
is
te
s
ti
n
g
m
et
h
o
d
,
w
h
ic
h
d
ec
id
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o
n
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e
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ll
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o
th
e
s
is
i
f
th
e
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ec
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v
ed
s
a
m
p
les
f
o
llo
w
th
e
n
o
i
s
e
C
u
m
u
lati
v
e
Di
s
tr
i
b
u
tio
n
Fu
n
ctio
n
(
C
DF)
d
en
o
ted
as
F
0
.
L
et
x
[
k
]
d
en
o
te
th
e
s
et
o
f
N
d
is
cr
ete
ti
m
e
v
ec
to
r
o
b
s
er
v
atio
n
s
k
=1
,
2
….
N
.
L
et
t
h
e
i
th
co
m
p
o
n
en
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o
f
x
[
k]
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e
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en
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as
x
i
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k
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,
i=1
,
2
…M
.
T
h
e
s
i
g
n
a
l
d
etec
tio
n
in
n
o
is
e
is
t
h
er
ef
o
r
e
g
iv
e
n
as a
s
i
m
p
le
h
y
p
o
t
h
esi
s
t
esti
n
g
p
r
o
b
le
m
in
[
9
-
1
1
]
an
d
i
s
ex
p
r
ess
ed
as
Dec
id
e
o
n
H
0
:
if
(
)
=
0
(
)
Dec
id
e
o
n
H
1
:
if
(
)
≠
0
(
)
(
6
)
w
h
er
e,
F
n
(
x
)
is
t
h
e
e
m
p
ir
ical
C
DF o
f
t
h
e
r
ec
eiv
ed
s
a
m
p
le
.
T
h
e
p
o
p
u
lar
g
o
o
d
n
ess
o
f
f
it te
s
ts
ar
e
:
a.
Anderso
n
da
rling
(
AD)
t
est
T
o
test
th
e
n
o
r
m
alit
y
o
f
a
r
an
d
o
m
s
a
m
p
le
x
[
k
]
th
e
An
d
er
s
o
n
Dar
lin
g
test
s
tati
s
tic
f
o
r
m
u
l
ated
in
[
1
7
]
is
g
i
v
e
n
as:
A
n
2
=
−
−
∑
(
2k
−
1
)
(
ln
z
k
−
ln
z
(
N
+
1
−
k
)
)
=
1
(
7
)
w
h
e
n
t
h
e
m
ea
n
an
d
v
ar
ian
c
e
o
f
t
h
e
s
a
m
p
le
ar
e
u
n
k
n
o
w
n
t
h
e
ad
j
u
s
ted
A
D
s
tatis
ti
c
as
g
iv
e
n
i
n
[
18
]
is
A
=
A
n
2
(
1
+
0
.
75
+
2
.
25
2
)
(
8
)
w
h
er
e
z
k
=F
0
(y
k
)
is
t
h
e
ass
u
m
ed
d
is
tr
ib
u
tio
n
,
N
d
en
o
tes
th
e
s
a
m
p
le
s
ize,
ln
is
th
e
n
at
u
r
al
lo
g
ar
ith
m
w
it
h
=
(
−
̆
)
⁄
w
h
er
e
̌
=
∑
⁄
an
d
2
=
∑
(
−
̌
)
2
(
−
1
)
⁄
.
T
h
e
s
p
ec
tr
u
m
s
e
n
s
in
g
p
r
o
b
le
m
i
s
ex
p
r
ess
ed
as:
H
0
:
A
≤
λ
cv
H
1
:
A
>
λ
cv
(
9
)
w
h
er
e,
λ
cv
is
a
cr
itical
v
a
lu
e.
I
f
A
ex
ce
ed
s
th
e
cr
itical
v
al
u
e
th
e
n
H
0
is
r
ej
ec
ted
.
A
tab
le
o
f
th
r
es
h
o
ld
s
f
o
r
d
if
f
er
e
n
t v
a
lu
e
s
o
f
P
f
is
g
iv
e
n
in
[
1
9
].
b.
K
o
l
m
o
g
o
ro
v
-
s
m
ir
no
v
(
K
S)
t
est
I
n
th
e
KS te
s
t t
h
e
d
is
ta
n
ce
b
etw
ee
n
F
n
(
x
)
an
d
F0
(
x
)
is
g
iv
e
n
b
y
:
D
n
=
m
ax
|
(
)
−
0
(
)
|
(
1
0
)
w
h
er
e
F
n
(
x
)
i
s
th
e
e
m
p
ir
ical
d
i
s
tr
ib
u
tio
n
.
I
f
t
h
e
s
a
m
p
les
u
n
d
e
r
test
ar
e
co
m
in
g
f
r
o
m
F0
(
x
)
,
th
en
,
D
n
co
n
v
er
g
es
to
0
.
I
f
th
e
v
al
u
e
o
f
D
n
e
x
ce
ed
s
th
e
cr
itical
v
al
u
e
th
e
n
H
0
i
s
r
ej
ec
ted
.
A
tab
le
o
f
th
r
es
h
o
ld
s
f
o
r
d
if
f
er
e
n
t
v
al
u
e
s
o
f
P
f
is
g
i
v
e
n
in
[
2
0
].
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
A
s
ta
tis
tica
l a
p
p
r
o
a
ch
to
s
p
ec
t
r
u
m
s
en
s
in
g
u
s
in
g
b
a
ye
s
fa
cto
r
a
n
d
p
-
v
a
lu
es (
Dee
p
a
N
.
R
ed
d
y)
2913
bera
T
h
e
J
a
r
q
u
e
an
d
B
e
r
a
(
J
B
)
test
is
an
o
th
er
g
o
o
d
n
es
s
-
of
-
f
it
test
to
ch
ec
k
f
o
r
n
o
r
m
al
d
is
tr
ib
u
ti
o
n
.
I
t
u
s
e
s
th
e
s
k
e
w
n
e
s
s
a
n
d
k
u
r
to
s
is
to
d
eter
m
in
e
w
h
e
th
er
th
e
s
a
m
p
le
d
ata
is
f
r
o
m
a
n
o
r
m
al
d
is
tr
ib
u
tio
n
[2
1
]
.
T
h
e
JB
test
s
tati
s
tic
i
s
th
e
co
m
b
i
n
atio
n
o
f
t
h
e
s
q
u
ar
es o
f
n
o
r
m
a
lized
s
k
e
w
n
es
s
an
d
k
u
r
to
s
i
s
an
d
is
g
iv
e
n
as
f
o
llo
w
s
:
=
6
(
1
2
+
(
2
−
3
)
2
4
)
(
1
1
)
w
h
er
e,
1
is
t
h
e
s
k
e
w
n
es
s
a
n
d
2
i
s
th
e
k
u
r
to
s
i
s
a
n
d
N
i
s
t
h
e
n
u
m
b
er
o
f
s
a
m
p
le
s
.
T
h
e
cr
itical
v
alu
e
s
o
f
t
h
e
J
B
t
est
f
o
r
d
if
f
er
e
n
t
s
a
m
p
le
s
izes
ar
e
g
iv
e
n
in
[
2
1
]
.
T
h
e
p
r
im
ar
y
u
s
er
s
i
g
n
a
l
is
d
ec
lar
ed
p
r
esen
t
if
t
h
e
J
ar
q
u
e
B
er
a
test
s
tatis
tic
i
s
g
r
ea
ter
t
h
an
t
h
e
cr
itical
v
a
lu
e
an
d
is
d
ec
l
ar
ed
as
n
o
i
s
e
o
th
er
w
i
s
e.
T
h
e
s
p
ec
tr
u
m
s
en
s
i
n
g
p
r
o
b
lem
u
s
in
g
J
B
test
ca
n
b
e
ex
p
r
ess
ed
as
H
0
:
J
≤
λ
cv
H
1
:
J
>
λ
cv
(
1
2
)
2
.
2
.
Sig
nifica
nce
o
f
s
t
a
t
is
t
ica
l
m
e
a
s
ures
2
.
2
.
1
.
p
-
v
a
lue
Fis
h
er
j
u
s
ti
f
ied
t
h
at
t
h
e
p
-
V
a
l
u
e
ca
n
b
e
v
ie
w
ed
a
s
an
in
d
e
x
o
f
th
e
“stre
n
g
t
h
o
f
ev
id
e
n
ce
”
ag
ain
s
t
H
0
,
w
it
h
s
m
all
p
i
n
d
ic
atin
g
an
u
n
l
ik
el
y
h
y
p
o
t
h
esi
s
[
1
2
]
.
T
h
e
tes
t
s
tat
is
tic
is
u
s
ed
to
d
eter
m
i
n
e
th
e
p
-
Va
lu
e
u
s
i
n
g
th
e
f
o
r
m
u
la
m
e
n
tio
n
ed
in
T
ab
le
1
as
g
iv
e
n
in
[
18
]
an
d
th
e
in
ter
p
r
etatio
n
o
f
th
e
test
r
esu
lt
s
ar
e
g
i
v
en
in
T
ab
le
2
.
T
ab
le
1
.
T
h
e
p
-
v
alu
e
f
o
r
m
u
la
f
o
r
An
d
er
s
o
n
da
r
lin
g
tes
t
A
D
st
a
t
i
st
i
c
p
-
V
a
l
u
e
F
o
r
mu
l
a
A
>
1
5
3
.
4
6
7
=
0
0
.
6
<
A
≤
1
5
3
.
4
6
7
=
(
1
.
2
9
3
7
−
5
.
709
∗
+
0
.
0186
2
)
0
.
3
4
<
A
≤
0
.
6
0
=
(
0
.
9
1
7
7
−
4
.
2
7
9
∗
−
1
.
38
2
)
0
.
2
0
<
A
≤
0
.
3
4
=
1
−
(
−
8
.
318
+
42
.
7
9
6
∗
−
59
.
9
3
8
2
)
A
≤
0
.
2
0
=
1
−
(
−
13
.
4
3
6
+
1
0
1
.
14
∗
−
223
.
73
2
)
T
ab
le
2
.
Dec
is
io
n
t
ab
le
M
e
t
h
o
d
C
o
n
d
i
t
i
o
n
D
e
c
i
si
o
n
C
l
a
ssi
c
a
l
t
e
st
i
f
(
t
e
st
s
t
a
t
i
s
t
i
c
>
C
r
i
t
i
c
a
l
v
a
l
u
e
)
H
0
i
s re
j
e
c
t
e
d
C
l
a
ssi
c
a
l
t
e
st
i
f
(
t
e
st
s
t
a
t
i
s
t
i
c
<
C
r
i
t
i
c
a
l
v
a
l
u
e
)
H
0
c
a
n
n
o
t
b
e
r
e
j
e
c
t
e
d
p
–
V
a
l
u
e
(
p
-
V
a
l
u
e
<
α)
H
0
i
s re
j
e
c
t
e
d
p
-
V
a
l
u
e
(
p
-
V
a
l
u
e
>
α)
H
0
c
a
n
n
o
t
b
e
r
e
j
e
c
t
e
d
T
h
e
s
tep
s
in
v
o
l
v
ed
in
h
y
p
o
t
h
e
s
is
tes
tin
g
u
s
i
n
g
p
-
Val
u
es
g
i
v
e
n
in
[
1
2
]
ar
e
as
f
o
llo
w
s
:
a.
Def
i
n
e
th
e
n
u
ll a
n
d
alter
n
ati
v
e
h
y
p
o
th
e
s
es.
b.
C
o
m
p
u
t
e
th
e
te
s
t sta
tis
tic
f
r
o
m
th
e
s
a
m
p
le
d
ata.
c.
Dete
r
m
i
n
e
th
e
p
-
Val
u
e
u
s
in
g
t
h
e
test
s
tatis
tic
o
b
tain
ed
f
r
o
m
s
tep
2
.
d.
Fix
t
h
e
s
i
g
n
i
f
i
c
an
c
e
le
v
el
α
=0
.
0
5
an
d
in
ter
p
r
et
th
e
r
esu
lt
s
u
s
i
n
g
T
ab
le
2
.
2
.
2
.
2
.
B
a
y
es
f
a
c
t
o
r
m
et
ho
d
T
h
e
ter
m
B
ay
e
s
f
ac
to
r
(
B
F)
is
also
ca
lled
as
lik
elih
o
o
d
r
atio
.
T
h
e
B
ay
es
f
ac
to
r
is
o
f
ten
r
ef
er
r
ed
t
o
as
th
e
“
s
tr
en
g
t
h
o
f
e
v
id
e
n
ce
”
o
r
“w
e
ig
h
t
o
f
e
v
id
en
ce
”.
B
ay
e
s
f
ac
to
r
s
s
h
o
w
th
at
p
-
V
alu
e
s
g
r
ea
tl
y
o
v
er
s
tate
t
h
e
ev
id
en
ce
a
g
ain
s
t
t
h
e
n
u
ll
h
y
p
o
t
h
esi
s
.
T
h
e
B
ay
es
f
ac
to
r
s
h
a
v
e
d
ir
ec
t
in
ter
p
r
etatio
n
as
th
e
s
tr
en
g
th
o
f
th
e
ev
id
e
n
ce
in
f
a
v
o
r
o
f
H
1
r
elativ
e
to
H
0.
T
h
e
u
s
e
o
f
B
a
y
es
f
ac
to
r
s
ca
n
av
o
id
th
e
m
is
i
n
ter
p
r
etatio
n
s
th
at
ar
is
e
f
r
o
m
d
ep
en
d
en
c
y
o
n
th
e
p
-
Va
lu
e
in
d
ec
is
io
n
s
[
1
3
,
1
4
]
.
Min
im
u
m
B
a
y
e
s
f
ac
to
r
s
h
av
e
t
h
e
a
d
v
an
ta
g
e
th
a
t
th
e
y
d
o
n
o
t
d
ep
en
d
o
n
th
e
p
r
io
r
p
r
o
b
ab
ilit
y
.
T
h
e
p
r
o
o
f
o
f
th
e
m
i
n
i
m
u
m
B
a
y
e
s
f
ac
to
r
as
f
u
n
cti
o
n
o
f
th
e
p
-
Valu
e
i
s
g
iv
e
n
i
n
[
1
4
]
.
Fig
u
r
e
3
s
h
o
w
n
C
ateg
o
r
izatio
n
o
f
B
a
y
es
Facto
r
s
B
F<1
in
to
lev
els o
f
ev
id
e
n
c
e
ag
ain
s
t H
0
.
(
)
=
{
−
<
1
1
ℎ
}
(
1
3
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
9
,
No
.
4
,
A
u
g
u
s
t 2
0
1
9
:
2
9
1
0
-
2917
2914
T
ab
le
3
.
C
ateg
o
r
izatio
n
o
f
b
a
y
es f
ac
to
r
s
B
F<1
in
to
le
v
els o
f
ev
id
en
ce
a
g
ain
s
t H
0
as
g
iv
e
n
i
n
[
1
4
]
3.
P
RO
P
O
SE
D
M
E
T
H
O
D
T
h
e
b
lo
ck
d
iag
r
am
o
f
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
is
g
iv
e
n
in
Fi
g
u
r
e
1
.
T
h
is
p
ap
er
a
d
o
p
ts
th
e
f
o
llo
w
in
g
s
tatis
t
ical
m
et
h
o
d
s
to
i
n
te
g
r
at
e
th
e
s
tat
is
tical
m
ea
s
u
r
es
f
r
o
m
i
n
d
ep
en
d
en
t
te
s
ts
[
2
2
-
25
]
to
h
a
v
e
a
n
o
v
er
al
l
ass
es
s
m
en
t o
n
th
e
d
etec
tio
n
o
f
th
e
p
r
i
m
ar
y
u
s
er
s
i
g
n
al
ac
ti
v
it
y
.
Fig
u
r
e
1
.
B
lo
ck
d
iag
r
a
m
o
f
t
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
3
.
1
.
p
-
v
a
lue ba
s
ed
div
er
s
it
y
co
m
bin
er
T
h
e
M
in
d
ep
en
d
en
t
s
a
m
p
les
x
i
[
k
]
i
=1
,
2
…
M
ar
e
r
ec
eiv
ed
f
r
o
m
M
d
iv
er
s
it
y
b
r
an
c
h
es
o
f
th
e
C
R
r
ec
eiv
er
.
T
h
e
test
s
tati
s
tic
s
(
A
1
,A
2
….
A
M
)
an
d
it
s
co
r
r
esp
o
n
d
in
g
p
-
V
a
lu
e
s
(p
1
,p
2
….
.
p
M
)
ar
e
co
m
p
u
ted
.
T
h
is
s
tu
d
y
ad
o
p
ts
th
e
f
o
llo
w
i
n
g
s
t
atis
tical
m
eth
o
d
s
to
in
te
g
r
ate
th
e
p
-
Valu
e
s
f
r
o
m
i
n
d
ep
en
d
en
t
test
s
[
2
2
,
2
3
]
to
h
av
e
a
n
o
v
er
all
as
s
es
s
m
e
n
t o
n
th
e
d
etec
tio
n
o
f
t
h
e
p
r
i
m
ar
y
u
s
er
s
ig
n
al
ac
ti
v
it
y
.
3
.
1
.
1
.
F
is
her’
s
t
est
A
p
o
p
u
lar
m
et
h
o
d
o
f
co
m
b
i
n
i
n
g
t
h
e
p
-
V
al
u
es
i
s
th
e
Fi
s
h
er
’
s
m
et
h
o
d
[
2
2
]
.
L
et
p
1,
p
2
,
…,
p
M
b
e
th
e
s
ig
n
i
f
ica
n
ce
p
r
o
b
ab
ilit
ies
o
f
th
e
test
s
tatis
t
ic
A
o
r
J
f
r
o
m
t
h
e
i
th
s
a
m
p
le
r
ec
eiv
ed
f
r
o
m
ea
c
h
d
iv
er
s
it
y
b
r
an
c
h
o
f
th
e
C
R
r
ec
eiv
er
.
T
h
e
j
o
in
t
ass
ess
m
en
t
o
f
t
h
e
n
o
r
m
a
lit
y
is
b
a
s
ed
o
n
th
e
M
v
al
u
es
o
f
th
e
s
ta
tis
tic.
T
h
e
d
if
f
er
en
t
s
ig
n
i
f
ica
n
ce
p
r
o
b
ab
ilit
ies
o
b
tain
ed
f
r
o
m
M
d
i
v
er
s
it
y
b
r
an
c
h
es
ar
e
co
m
b
in
ed
u
s
i
n
g
Fi
s
h
er
's
m
et
h
o
d
as
g
i
v
en
b
elo
w
.
=
−
2
(
∑
=
1
)
(
1
4
)
3
.
2
.
B
a
y
es F
a
ct
o
r
ba
s
ed
div
er
s
it
y
co
m
bi
ner
Fro
m
th
e
v
al
u
es
(p
1
,p
2
….
.
p
M
)
th
eir
co
r
r
esp
o
n
d
in
g
Mi
n
i
m
u
m
B
a
y
es
f
ac
to
r
s
ar
e
co
m
p
u
ted
f
r
o
m
(
1
3
)
.
I
n
th
e
co
n
te
x
t
o
f
s
p
ec
tr
u
m
s
e
n
s
i
n
g
th
e
f
o
llo
w
in
g
m
eth
o
d
i
s
p
r
o
p
o
s
ed
to
in
teg
r
ate
th
e
s
e
s
tatis
t
ical
m
ea
s
u
r
e
s
f
r
o
m
i
n
d
ep
en
d
en
t
test
s
[
24
]
to
h
av
e
an
o
v
er
all
a
s
s
es
s
m
en
t
o
n
th
e
d
etec
tio
n
o
f
t
h
e
p
r
i
m
ar
y
u
s
er
s
i
g
n
a
l
ac
tiv
i
t
y
.
T
h
e
m
eth
o
d
p
r
o
p
o
s
ed
f
o
r
co
m
b
in
i
n
g
t
h
e
d
ata
i
s
b
y
ca
lc
u
l
atin
g
t
h
e
p
r
o
d
u
ct
o
f
t
h
e
B
a
y
es
f
ac
to
r
ca
lc
u
lated
f
r
o
m
M
i
n
d
ep
en
d
en
t
s
a
m
p
le
s
an
d
is
d
ef
i
n
ed
as th
e
Gr
o
u
p
B
a
y
es
Facto
r
(
GB
F)
as g
i
v
e
n
in
[
2
4
].
=
∏
,
(
)
=
1
(
1
5
)
w
h
er
e,
th
e
s
u
b
s
cr
ip
ts
i,j
r
ef
er
to
th
e
h
y
p
o
t
h
esi
s
m
o
d
el
s
b
ein
g
co
m
p
ar
ed
,
an
d
t
h
e
b
r
ac
k
ete
d
s
u
p
er
s
cr
ip
t
r
ef
er
s
to
th
e
M
-
t
h
s
a
m
p
le.
Sin
ce
t
h
e
m
ea
s
u
r
ed
d
ata
is
tr
ea
ted
as
co
n
d
itio
n
all
y
i
n
d
ep
en
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en
t
s
a
m
p
l
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th
e
p
r
o
b
ab
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ies
ar
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m
u
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ip
lied
.
S
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t
h
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Ev
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
A
s
ta
tis
tica
l a
p
p
r
o
a
ch
to
s
p
ec
t
r
u
m
s
en
s
in
g
u
s
in
g
b
a
ye
s
fa
cto
r
a
n
d
p
-
v
a
lu
es (
Dee
p
a
N
.
R
ed
d
y)
2915
Alg
o
rit
h
m
1
.
Sta
t
is
t
ica
l a
pp
r
o
a
ch
t
o
s
pect
ru
m
s
e
ns
i
ng
1.
Ob
tain
M
o
b
s
er
v
atio
n
s
a
m
p
les
f
r
o
m
ea
ch
o
f
th
e
d
i
v
er
s
it
y
b
r
an
ch
e
s
o
f
t
h
e
C
R
n
o
d
e.
2.
L
et
Z
i
,
(
i=1
…
M
)
b
e
t
h
e
o
b
s
er
v
atio
n
v
ec
to
r
.
So
r
t th
e
o
b
s
er
v
at
io
n
s
f
r
o
m
ea
ch
b
r
an
c
h
in
a
s
ce
n
d
in
g
o
r
d
er
.
3.
C
alcu
late
th
e
AD
test
s
tatis
t
ic
u
s
i
n
g
E
q
u
atio
n
(
7
)
an
d
(
8
)
.
L
et
A
i
(
i
=1
…
M
)
d
en
o
te
th
e
test
s
tatis
t
ic
o
b
tain
ed
f
o
r
M
d
iv
er
s
it
y
b
r
an
c
h
es.
4.
Usi
n
g
t
h
e
f
o
r
m
u
la
g
i
v
en
i
n
T
a
b
le
1
ca
lcu
late
th
e
p
-
Val
u
e
p
1
p
2
,
…
, p
M
an
d
th
eir
r
esp
ec
tiv
e
MB
Fs
u
s
i
n
g
E
q
u
atio
n
(
1
3
)
.
5.
T
h
e
MB
Fs
an
d
p
-
Valu
e
s
f
r
o
m
M
d
iv
er
s
it
y
b
r
an
ch
e
s
ar
e
co
m
b
in
ed
u
s
i
n
g
E
q
u
atio
n
(
1
4
)
an
d
(
1
5
)
t
o
o
b
tain
th
e
n
e
w
d
ec
i
s
io
n
s
tatis
t
ic.
6
R
ej
ec
t n
u
ll H
y
p
o
th
e
s
is
i
f
t
h
e
n
e
w
d
ec
is
io
n
s
tati
s
tic
i
s
less
t
h
a
n
th
e
p
r
ed
ef
i
n
ed
s
i
g
n
i
f
ica
n
ce
l
ev
el.
4.
RE
SU
L
T
S
A
ND
AN
AL
Y
SI
S
4
.
1
.
M
o
nte
ca
rlo
s
i
m
ula
t
io
ns
T
h
e
p
er
f
o
r
m
a
n
ce
a
n
al
y
s
i
s
o
f
s
p
ec
tr
u
m
s
en
s
in
g
u
s
in
g
r
ec
e
iv
er
d
iv
er
s
it
y
i
n
a
C
R
en
v
ir
o
n
m
e
n
t
ar
e
ca
r
r
ied
o
u
t
u
s
in
g
1
)
C
o
n
v
e
n
ti
o
n
al
H
y
p
o
t
h
esi
s
T
esti
n
g
a
n
d
2)
Statis
tical
H
y
p
o
t
h
esi
s
tes
ti
n
g
.
T
h
e
s
tati
s
tical
h
y
p
o
t
h
esi
s
test
i
n
g
is
ca
r
r
ied
o
u
t u
s
i
n
g
t
h
e
f
o
llo
w
i
n
g
t
w
o
m
e
th
o
d
s
1
)
p
-
Valu
e
s
2
)
Min
i
m
u
m
B
a
y
es
Facto
r
.
T
h
e
d
etec
tio
n
p
r
o
b
ab
ilit
y
i
s
u
s
ed
as
a
s
tan
d
ar
d
o
f
m
ea
s
u
r
e
m
en
t
to
d
eter
m
i
n
e
t
h
e
s
en
s
i
n
g
ac
cu
r
ac
y
.
T
h
e
f
o
llo
w
i
n
g
a
s
s
u
m
p
t
io
n
s
ar
e
m
ad
e
in
t
h
e
s
i
m
u
latio
n
s
.
a.
T
h
e
s
y
s
te
m
m
o
d
el
h
as Si
n
g
le
I
n
p
u
t M
u
ltip
le
Ou
tp
u
t.
b.
T
h
e
p
r
im
ar
y
tr
a
n
s
m
itter
s
ig
n
al
is
a
s
in
u
s
o
id
al
p
ilo
t si
g
n
a
l o
f
k
n
o
w
n
f
r
eq
u
en
c
y
.
c.
A
d
d
iti
v
e
W
h
ite
Ga
u
s
s
ian
No
i
s
e
w
it
h
μ
=0
an
d
σ
2
=1
.
d.
Fo
r
th
e
H
y
p
o
th
e
s
is
H
1
to
b
e
d
ec
lar
ed
tr
u
e
(
s
ig
n
a
l
is
p
r
esen
t)
Me
th
o
d
1
: I
f
th
e
p
-
Val
u
e
s
is
le
s
s
t
h
an
t
he
s
i
g
n
i
f
ican
ce
le
v
el
α
=0
.
0
5
Me
th
o
d
2
: I
f
th
e
m
in
i
m
u
m
B
ay
es
f
ac
to
r
is
les
s
th
a
n
1
/1
0
0
Fig
u
r
e
2
p
r
o
v
id
es
a
co
m
p
ar
is
o
n
o
f
th
e
Go
o
d
n
ess
o
f
f
i
t
tes
ts
in
th
e
co
n
te
x
t
o
f
p
r
i
m
ar
y
u
s
er
s
i
g
n
a
l
d
etec
tio
n
i
n
co
g
n
i
tiv
e
r
ad
io
.
T
h
e
C
o
n
v
e
n
tio
n
a
l
m
et
h
o
d
o
f
h
y
p
o
th
e
s
is
te
s
tin
g
in
a
C
R
en
v
ir
o
n
m
e
n
t
is
co
m
p
ar
ed
w
it
h
th
e
s
tati
s
tical
m
et
h
o
d
o
f
h
y
p
o
t
h
esi
s
test
i
n
g
.
T
h
e
n
u
m
b
er
o
f
s
a
m
p
le
s
i
n
t
h
e
test
is
ta
k
e
n
as
1
0
0
.
T
h
e
test
s
in
c
lu
d
e
th
e
th
e
co
n
v
en
t
io
n
al
en
er
g
y
d
etec
to
r
,
An
d
er
s
o
n
Dar
l
in
g
te
s
t,
Ko
l
m
o
g
o
r
o
v
-
S
m
ir
n
o
v
T
est
an
d
J
ar
q
u
e
an
d
B
er
a
T
est.
E
n
er
g
y
d
etec
to
r
s
h
o
w
s
b
etter
p
er
f
o
r
m
an
ce
co
m
p
ar
ed
to
th
e
o
th
er
test
s
.
B
u
t
A
D
test
p
r
o
v
id
es
b
etter
d
etec
tio
n
p
er
f
o
r
m
a
n
ce
co
m
p
ar
ed
to
th
e
o
th
er
n
o
r
m
ali
t
y
tes
ts
.
T
he
s
tatis
t
ical
m
ea
s
u
r
e
co
in
ed
as p
-
Val
u
e
an
d
B
a
y
es
Facto
r
ar
e
u
s
ed
as st
atis
tical
m
ea
s
u
r
es i
n
h
y
p
o
th
e
s
is
te
s
ti
n
g
i
n
Fi
g
u
r
e
s
3
-
6
.
Fig
u
r
e
2
.
Sp
ec
tr
u
m
s
e
n
s
in
g
u
s
in
g
Go
o
d
n
ess
o
f
f
it te
s
ts
w
h
e
n
th
e
p
r
i
m
ar
y
u
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I
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2917
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[1
]
S.
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tap
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t
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s
a
c
ti
o
n
s
o
n
Veh
icu
l
a
r
T
e
c
h
n
o
l
o
g
y
,
v
o
l/
issu
e
:
5
9
(
4
),
p
p
.
1
7
9
1
-
8
0
0
,
2
0
1
0
.
[5
]
Dig
h
a
m
F
.
F
.
,
e
t
a
l.
,
“
On
th
e
e
n
e
rg
y
d
e
te
c
ti
o
n
o
f
u
n
k
n
o
w
n
sig
n
a
ls
o
v
e
r
fa
d
in
g
c
h
a
n
n
e
ls,”
IEE
E
t
ra
n
sa
c
ti
o
n
s
o
n
c
o
mm
u
n
ica
t
io
n
s
,
v
o
l.
5
5
,
p
p
.
21
-
2
4
,
2
0
0
7
.
[6
]
A
n
n
a
v
a
jj
a
la
R
.
,
e
t
a
l.
,
“
P
e
rf
o
r
m
a
n
c
e
a
n
a
l
y
sis
o
f
li
n
e
a
r
d
iv
e
rsit
y
-
c
o
m
b
in
in
g
sc
h
e
m
e
s
o
n
Ra
y
lei
g
h
fa
d
in
g
c
h
a
n
n
e
ls
w
it
h
b
in
a
ry
sig
n
a
li
n
g
a
n
d
G
a
u
ss
ian
w
e
ig
h
ti
n
g
e
rro
rs
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
W
ire
les
s
Co
mm
u
n
i
c
a
ti
o
n
s,
v
o
l.
4,
pp.
2
2
6
7
-
7
8
,
2
0
0
5
.
[7
]
He
ra
th
S
.
P
.
,
e
t
a
l
.
,
“
On
th
e
e
n
e
rg
y
d
e
te
c
ti
o
n
o
f
u
n
k
n
o
w
n
d
e
term
in
isti
c
sig
n
a
l
o
v
e
r
Na
k
a
g
a
m
i
c
h
a
n
n
e
ls
w
it
h
se
l
e
c
ti
o
n
c
o
m
b
in
in
g
,
”
IEE
E
Ca
n
a
d
ia
n
c
o
n
fer
e
n
c
e
o
n
th
e
El
e
c
trica
l
a
n
d
Co
m
p
u
ter
E
n
g
i
n
e
e
rin
g
,
p
p
.
7
4
5
-
7
4
9
,
2
0
0
9
.
[8
]
Ak
b
a
ri
M
.
,
e
t
a
l.
,
“
Re
c
e
iv
e
r
d
iv
e
rsit
y
c
o
m
b
in
in
g
u
sin
g
e
v
o
lu
ti
o
n
a
ry
a
l
g
o
rit
h
m
s
in
ra
y
lei
g
h
f
a
d
in
g
c
h
a
n
n
e
l
,
”
T
h
e
S
c
ien
t
if
ic W
o
rld
J
o
u
r
n
a
l
,
2
0
1
4
.
[9
]
A
rsh
a
d
K
.
,
e
t
a
l.
,
“
Ro
b
u
st
s
p
e
c
tru
m
se
n
sin
g
b
a
se
d
o
n
sta
ti
stica
l
t
e
sts,”
IET
Co
mm
u
n
ic
a
ti
o
n
s,
v
o
l
.
7
,
p
p
.
8
0
8
-
1
7
,
2
0
1
3
.
[1
0
]
Ca
rv
a
lh
o
F
.
B
.
,
e
t
a
l.
,
“
C
o
g
n
it
iv
e
sp
e
c
tru
m
s
e
n
sin
g
b
a
se
d
o
n
sta
ti
stica
l
tes
ts
in
fa
d
in
g
c
h
a
n
n
e
ls,”
Co
mm
u
n
ic
a
ti
o
n
s
(
L
AT
INCOM
),
7
t
h
IEE
E
L
a
ti
n
-
A
me
ric
a
n
Co
n
fer
e
n
c
e
,
p
p
.
1
-
6
,
2
0
1
5
.
[1
1
]
H.
W
a
n
g
,
e
t
a
l.
,
“
S
p
e
c
tru
m
se
n
sin
g
in
c
o
g
n
i
ti
v
e
ra
d
i
o
u
si
n
g
g
o
o
d
n
e
ss
o
f
f
it
tes
ti
n
g
,”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
W
ire
les
s Co
mm
u
n
ica
ti
o
n
s
,
v
o
l/
issu
e
:
8
(
11
),
2
0
0
9
.
[1
2
]
Be
rg
e
r
J
.
O
.
,
“
Co
u
ld
F
ish
e
r,
Je
f
fre
y
s
a
n
d
Ne
y
m
a
n
h
a
v
e
a
g
re
e
d
o
n
tes
ti
n
g
?
”
S
ta
ti
stica
l
S
c
ien
c
e
,
v
o
l.
1
8
,
p
p
.
1
-
3
2
,
2
0
0
3
.
[1
3
]
Ka
tk
i
H
.
A
.
,
“
In
v
it
e
d
c
o
m
m
e
n
tary
:
e
v
id
e
n
c
e
-
b
a
se
d
e
v
a
lu
a
ti
o
n
o
f
p
v
a
lu
e
s
a
n
d
Ba
y
e
s
f
a
c
to
rs
,
”
Ame
r
ica
n
J
o
u
rn
a
l
o
f
Ep
id
e
mi
o
lo
g
y
,
v
o
l.
1
6
8
,
p
p
.
3
8
4
-
3
8
8
,
2
0
0
8
.
[1
4
]
He
ld
L
.
,
e
t
a
l
.
,
“
Ho
w
th
e
m
a
x
i
m
a
l
e
v
id
e
n
c
e
o
f
p
-
v
a
lu
e
s
a
g
a
in
st
p
o
i
n
t
n
u
ll
h
y
p
o
th
e
se
s
d
e
p
e
n
d
s
o
n
sa
m
p
le
siz
e
,
”
T
h
e
Ame
ric
a
n
S
t
a
ti
sticia
n
,
v
o
l.
7
0
,
p
p
.
3
3
5
-
4
1
,
2
0
1
6
.
[1
5
]
G
o
o
d
m
a
n
S
.
N
.
,
“
T
o
w
a
rd
e
v
id
e
n
c
e
-
b
a
se
d
m
e
d
ica
l
sta
ti
stics
.
1
:
T
h
e
P
v
a
lu
e
fa
ll
a
c
y
,
”
An
n
a
ls
o
f
i
n
te
rn
a
l
me
d
icin
e
,
v
o
l.
1
3
0
,
p
p
.
9
9
5
-
1
0
0
4
,
1
9
9
9
.
[1
6
]
S
.
N.
G
o
o
d
m
a
n
,
“
T
o
w
a
rd
e
v
id
e
n
c
e
-
b
a
se
d
m
e
d
ica
l
s
tatisti
c
s.
2
:
T
h
e
Ba
y
e
s
f
a
c
to
r
,”
An
n
a
ls
o
f
i
n
t
e
rn
a
l
me
d
icin
e
,
v
o
l.
1
3
0
,
p
p
.
1
0
0
5
-
1
0
1
3
,
1
9
9
9
.
[1
7
]
Ro
m
e
u
J
.
L
.
,
“
A
n
d
e
rso
n
-
Da
rli
n
g
:
a
g
o
o
d
n
e
ss
o
f
f
it
tes
t
f
o
r
sm
a
ll
sa
m
p
les
a
s
su
m
p
ti
o
n
s,”
RA
C
S
T
AR
T
,
2
0
0
3
.
[1
8
]
R
.
B
.
D
'
A
g
o
s
t
i
n
o
a
n
d
S
tep
h
e
n
s
M
.
A.
,
“
G
o
o
d
n
e
ss
-
of
-
f
it
tec
h
n
iq
u
e
s
,”
S
tatisti
c
s:
T
e
x
t
b
o
o
k
s
a
n
d
m
o
n
o
g
ra
p
h
,
Bo
c
a
Ra
to
n
,
F
lo
ri
d
a
:
CRC
P
re
ss
,
1
9
8
6
.
[1
9
]
S
tep
h
e
n
s
M
.
A
.
,
“
EDF
sta
ti
stics
f
o
r
g
o
o
d
n
e
ss
o
f
f
it
a
n
d
s
o
m
e
c
o
m
p
a
riso
n
s,”
J
o
u
rn
a
l
o
f
t
h
e
Ame
ric
a
n
st
a
ti
stica
l
Asso
c
ia
t
io
n
,
v
o
l.
6
9
,
p
p
.
7
3
0
-
7
,
1
9
7
4
.
[2
0
]
S.
F
a
c
c
h
in
e
tt
i
,
“
A
p
ro
c
e
d
u
re
t
o
f
in
d
e
x
a
c
t
c
rit
ica
l
v
a
lu
e
s
o
f
K
o
lm
o
g
o
ro
v
-
S
m
irn
o
v
tes
t
,”
S
ta
ti
st
ica
Ap
p
li
c
a
t
a
,
v
o
l.
21
,
p
p
.
3
3
7
-
3
5
9
,
2
0
0
9
.
[2
1
]
T
.
T
h
a
d
e
wa
ld
a
n
d
H
.
Bü
n
i
n
g
,
“
Ja
rq
u
e
–
Be
ra
tes
t
a
n
d
it
s
c
o
m
p
e
ti
to
rs
f
o
r
tes
ti
n
g
n
o
r
m
a
li
t
y
–
a
p
o
w
e
r
c
o
m
p
a
riso
n
,”
J
o
u
rn
a
l
o
f
A
p
p
l
ied
S
ta
ti
st
ics
,
v
o
l.
3
4
,
p
p
.
87
-
1
0
5
,
2
0
0
7
.
[2
2
]
Bh
a
n
d
a
ry
M
.
,
e
t
a
l.
,
“
Co
m
p
a
riso
n
o
f
se
v
e
ra
l
tes
ts
f
o
r
c
o
m
b
in
in
g
se
v
e
ra
l
in
d
e
p
e
n
d
e
n
t
tes
ts,”
J
o
u
r
n
a
l
o
f
M
o
d
e
r
n
Ap
p
li
e
d
S
ta
ti
st
ica
l
M
e
th
o
d
s,
v
o
l.
1
0
,
2
0
1
1
.
[2
3
]
W
h
it
lo
c
k
M
.
C
.
,
“
Co
m
b
in
i
n
g
p
r
o
b
a
b
i
li
ty
f
ro
m
in
d
e
p
e
n
d
e
n
t
tes
ts:
th
e
w
e
ig
h
ted
Z
‐
m
e
th
o
d
is
su
p
e
rio
r
to
F
ish
e
r'
s
a
p
p
ro
a
c
h
,
”
J
o
u
rn
a
l
o
f
e
v
o
l
u
ti
o
n
a
ry
b
io
l
o
g
y
,
vol
.
1
8
,
p
p
.
1
3
6
8
-
73,
2
0
0
5
.
[2
4
]
S
tep
h
a
n
K
.
E
.
,
e
t
a
l.
,
“
Ba
y
e
sia
n
m
o
d
e
l
se
lec
ti
o
n
f
o
r
g
ro
u
p
st
u
d
ies
.
Ne
u
ro
im
a
g
e
,
”
v
o
l.
4
6
,
p
p
.
1
0
0
4
-
1
7
,
2
0
0
9
.
[2
5
]
P
e
tt
i
tt
A
.
N
.
,
“
T
e
stin
g
th
e
n
o
rm
a
l
it
y
o
f
se
v
e
ra
l
in
d
e
p
e
n
d
e
n
t
sa
m
p
les
u
sin
g
t
h
e
A
n
d
e
rso
n
-
Da
rli
n
g
sta
t
isti
c
,
”
Ap
p
li
e
d
S
ta
ti
st
ics
,
p
p
.
1
5
6
-
6
1
,
1
9
7
7
.
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