Environmental Engineering Reference
In-Depth Information
1.5.5 Some practical observations
1.5.5.1 Choice of z
Previously, we have chosen z = 0.7, which is recommended in Slifker and Shapiro (1980). It
turns out that this choice is robust. Figure 1.23 shows the variation (with respect to z ) of
the identified distribution type and ( a X , b X , a Y , b Y ) for one realization of the simulated Y
data. For plotting on a numerical scale, SU is indexed as “1,” SB is indexed as “2,” and SL
is indexed as “3.” The values for the actual Y distribution (namely, type = 1, a X = 1, b X = −1,
a Y = 1, and b Y = 0) are plotted as dashed lines. The effect of sample size is illustrated in the
subplots using n = 30, 100, and 1000. When n = 30, the identified type is incorrect regard-
less of the choice of z . This false identification is less severe when n = 100. However, we
still see few false identifications when z is near 0.4. There is no false identification when
n = 1000. The identified ( a X , b X , a Y , b Y ) for n = 30 are very different from the actual values
because the type has been identified wrongly. The effect of these parameters on the PDF is
dependent on the probability model ( Figure 1.20 ) . The identified ( a X , b X , a Y , b Y ) for n = 100
are close to the actual values for z = 0.5-0.8. The identified ( a X , b X , a Y , b Y ) for n = 1000
are close to the actual values for a broad range of z . In general, z = 0.7 seems to be a robust
choice for n = 100 and 1000. Note that the subplot for n = 30 and possibly n = 100 will
change from realization to realization due to statistical uncertainty.
(a)
(b)
(c)
3
3
3
2
2
2
1
1
1
0
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0
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z
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z
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z
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z
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z
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2
0
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-4
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-4
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1
z
z
z
n = 30
n = 100
n = 1000
Figure 1.23 Variations of the identified type and ( a X , b X , a Y , b Y ) for one realization of the simulated Y data
with (a) n = 30; (b) n = 100; and (c) n = 1000.
 
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