The U(−1/2, 1/2) Wilson and Stevens intervals are similarly affected by the randomization for small n: when n = 10
and X = 2 the conditional expected length of the Stevens interval is 0.48 and the range of the upper bound is 0.111% or 23%
of the conditional expected length. However, the impact of the randomization on these interval decreases quicker than it
does for the split sample Wilson interval. When n = 200 and X = 40 the conditional expected length of the Stevens interval
is 0.11. The range of the upper bound is 0.005, which is no more than 4.5% of the conditional expected length.
Fig. 4 shows the range of the upper bounds of the three intervals for different combinations of X and n. The intervals are
about as bad for n = 20, but the U(−1/2, 1/2) Wilson and Stevens intervals are much less affected by the randomization
for n = 100 and n = 500. Because of the equivariance of the intervals, the figures for the lower bound are identical if X is
replaced by n − X.
4. Data-randomized intervals
The U(−1/2, 1/2) Wilson and Stevens intervals are similarly affected by the randomization for small n: when n = 10and X = 2 the conditional expected length of the Stevens interval is 0.48 and the range of the upper bound is 0.111% or 23%of the conditional expected length. However, the impact of the randomization on these interval decreases quicker than itdoes for the split sample Wilson interval. When n = 200 and X = 40 the conditional expected length of the Stevens intervalis 0.11. The range of the upper bound is 0.005, which is no more than 4.5% of the conditional expected length.Fig. 4 shows the range of the upper bounds of the three intervals for different combinations of X and n. The intervals areabout as bad for n = 20, but the U(−1/2, 1/2) Wilson and Stevens intervals are much less affected by the randomizationfor n = 100 and n = 500. Because of the equivariance of the intervals, the figures for the lower bound are identical if X isreplaced by n − X.4. Data-randomized intervals
การแปล กรุณารอสักครู่..