Table 4.

Coverage probabilities and (expected widths) of 95% PIs for the mean of a future sample size m.

		Wald	M-Wald	Likelihood	JS-PI	Wald	M-Wald	Likelihood	JS-PI
$\mu =3$
n	m	$\theta =.5$				$\theta =1$
20	5	.913(8.39)	.916(8.56)	.920(8.77)	.948(9.58)	.926(6.52)	.928(6.65)	.927(6.61)	.942(6.89)
30	5	.925(8.31)	.926(8.39)	.929(8.58)	.950(8.92)	.931(6.37)	.933(6.43)	.933(6.44)	.944(6.57)
40	10	.920(6.08)	.922(6.14)	.929(6.25)	.950(6.51)	.937(4.68)	.940(4.72)	.942(4.73)	.951(4.82)
50	30	.922(4.03)	.925(4.09)	.929(4.11)	.943(4.38)	.929(3.07)	.932(3.11)	.936(3.09)	.941(3.19)
70	20	.935(4.44)	.937(4.47)	.943(4.52)	.948(4.64)	.938(3.38)	.940(3.40)	.941(3.41)	.947(3.45)
90	50	.934(3.11)	.936(3.14)	.941(3.15)	.949(3.26)	.940(2.37)	.942(2.39)	.942(2.38)	.946(2.42)
n	m	$\theta =4$				$\theta =10$
20	5	.929(4.38)	.934(4.47)	.927(4.35)	.935(4.40)	.938(3.80)	.942(3.87)	.945(3.83)	.948(3.86)
30	5	.939(4.28)	.941(4.32)	.937(4.25)	.942(4.27)	.942(3.69)	.946(3.73)	.946(3.71)	.949(3.72)
40	10	.938(3.13)	.941(3.16)	.937(3.11)	.941(3.13)	.940(2.71)	.943(2.73)	.943(2.71)	.944(2.72)
50	30	.938(2.05)	.941(2.08)	.938(2.04)	.941(2.06)	.947(1.77)	.950(1.80)	.947(1.77)	.949(1.78)
70	20	.944(2.26)	.946(2.27)	.944(2.25)	.946(2.26)	.945(1.95)	.946(1.96)	.945(1.94)	.946(1.95)
90	50	.942(1.57)	.944(1.59)	.942(1.57)	.940(1.58)	.945(1.36)	.946(1.37)	.945(1.35)	.944(1.36)
$\mu =7$
n	m	$\theta =.5$				$\theta =1$
20	5	.909(18.78)	.911(19.1)	.914(19.5)	.948(21.3)	.929(14.0)	.931(14.3)	.931(14.2)	.943(14.8)
30	5	.923(18.46)	.924(18.6)	.927(19.0)	.952(19.8)	.928(13.7)	.929(13.8)	.934(13.9)	.944(14.1)
40	10	.925(13.69)	.927(13.8)	.933(14.0)	.952(14.6)	.937(10.1)	.939(10.2)	.940(10.2)	.951(10.4)
50	30	.925( 8.96)	.929( 9.1)	.934(9.17)	.952(9.74)	.936(6.65)	.940(6.75)	.940(6.69)	.947(6.89)
70	20	.936( 9.96)	.938(10.0)	.942(10.1)	.952(10.3)	.941(7.35)	.942(7.39)	.944(7.39)	.949(7.48)
90	50	.935( 6.95)	.937( 7.0)	.942(7.04)	.949(7.27)	.939(5.11)	.941(5.15)	.942(5.13)	.946(5.21)
n	m	$\theta =4$				$\theta =10$
20	5	.935(8.42)	.939(8.59)	.931(8.30)	.935(8.39)	.936(6.67)	.940(6.80)	.934(6.58)	.934(6.61)
30	5	.937(8.19)	.939(8.27)	.935(8.12)	.939(8.16)	.942(6.45)	.944(6.51)	.939(6.38)	.939(6.39)
40	10	.939(6.01)	.941(6.07)	.938(5.97)	.941(6.00)	.942(4.73)	.943(4.78)	.940(4.69)	.940(4.70)
50	30	.944(3.93)	.947(3.98)	.943(3.91)	.944(3.94)	.943(3.09)	.946(3.14)	.941(3.07)	.941(3.08)
70	20	.942(4.32)	.944(4.35)	.943(4.31)	.945(4.32)	.942(3.41)	.945(3.43)	.942(3.39)	.944(3.39)
90	50	.944(3.01)	.946(3.04)	.944(3.01)	.943(3.02)	.943(2.37)	.945(2.39)	.942(2.36)	.943(2.37)
$\mu =12$
n	m	$\theta =.5$				$\theta =1$
20	5	.909(31.6)	.912(32.3)	.915(33.1)	.947(36.0)	.924(23.3)	.926(23.7)	.927(23.7)	.943(24.6)
30	5	.919(31.3)	.920(31.5)	.925(32.5)	.948(33.6)	.938(22.9)	.940(23.1)	.942(23.1)	.952(23.6)
40	10	.930(22.9)	.931(23.1)	.935(23.6)	.954(24.6)	.936(16.9)	.938(17.0)	.941(17.0)	.948(17.4)
50	30	.927(15.1)	.930(15.3)	.934(15.5)	.947(16.4)	.928(11.0)	.931(11.2)	.935(11.1)	.942(11.4)
70	20	.932(16.8)	.934(16.9)	.940(17.1)	.952(17.5)	.935(12.2)	.936(12.3)	.939(12.3)	.945(12.4)
90	50	.938(11.7)	.940(11.8)	.943(12.0)	.951(12.3)	.939(8.55)	.941(8.62)	.944(8.59)	.948(8.72)
n	m	$\theta =4$				$\theta =10$
20	5	.928(13.2)	.933(13.5)	.926(13.0)	.929(13.2)	.934(9.90)	.938(10.1)	.930(9.72)	.931(9.76)
30	5	.941(12.9)	.942(13.0)	.940(12.8)	.942(12.8)	.942(9.63)	.944(9.72)	.939(9.50)	.940(9.52)
40	10	.944(9.54)	.946(9.63)	.943(9.47)	.942(9.51)	.942(7.06)	.944(7.13)	.940(6.99)	.939(7.00)
50	30	.944(6.23)	.948(6.32)	.945(6.19)	.945(6.23)	.945(4.61)	.949(4.67)	.943(4.57)	.943(4.58)
70	20	.944(6.83)	.945(6.87)	.944(6.80)	.946(6.82)	.941(5.08)	.942(5.11)	.940(5.05)	.941(5.05)
90	50	.947(4.77)	.949(4.80)	.947(4.75)	.947(4.77)	.943(3.54)	.944(3.57)	.941(3.53)	.943(3.53)