TABLE 3 Open in new tab The mean squared...

		MSE	Time	MSE	Time	MSE	Time	MSE	Time
OSMAC	0.0460	0.0213	0.0460	0.0410	0.0452	0.0875	0.0476	0.1679
SOS	$K = 1$	0.2346	0.0058	0.2320	0.0070	0.2300	0.0064	0.2274	0.0086
$K = 5$	0.0415	0.0101	0.0419	0.0090	0.0414	0.0124	0.0418	0.0150
$K = 10$	0.0410	0.0171	0.0405	0.0168	0.0419	0.0175	0.0406	0.0256
$K = 20$	0.0404	0.0318	0.0409	0.0322	0.0405	0.0378	0.0403	0.0437
$K = 40$	0.0401	0.0574	0.0398	0.0618	0.0400	0.0740	0.0400	0.0804

		MSE	Time	MSE	Time	MSE	Time	MSE	Time
OSMAC	0.0460	0.0213	0.0460	0.0410	0.0452	0.0875	0.0476	0.1679
SOS	$K = 1$	0.2346	0.0058	0.2320	0.0070	0.2300	0.0064	0.2274	0.0086
$K = 5$	0.0415	0.0101	0.0419	0.0090	0.0414	0.0124	0.0418	0.0150
$K = 10$	0.0410	0.0171	0.0405	0.0168	0.0419	0.0175	0.0406	0.0256
$K = 20$	0.0404	0.0318	0.0409	0.0322	0.0405	0.0378	0.0403	0.0437
$K = 40$	0.0401	0.0574	0.0398	0.0618	0.0400	0.0740	0.0400	0.0804

TABLE 3

The mean squared error (MSE) and time costs (in seconds) are obtained by the sequential one-step (SOS) and optimal sub-sampling methods motivated by the A-optimality criterion (OSMAC) methods for different sample sizes $N$ under the logistic regression model.

		MSE	Time	MSE	Time	MSE	Time	MSE	Time
Method		$N = 100, 000$		$N = 200, 000$		$N = 400, 000$		$N = 800, 000$
OSMAC		0.0460	0.0213	0.0460	0.0410	0.0452	0.0875	0.0476	0.1679
SOS	$K = 1$	0.2346	0.0058	0.2320	0.0070	0.2300	0.0064	0.2274	0.0086
	$K = 5$	0.0415	0.0101	0.0419	0.0090	0.0414	0.0124	0.0418	0.0150
	$K = 10$	0.0410	0.0171	0.0405	0.0168	0.0419	0.0175	0.0406	0.0256
	$K = 20$	0.0404	0.0318	0.0409	0.0322	0.0405	0.0378	0.0403	0.0437
	$K = 40$	0.0401	0.0574	0.0398	0.0618	0.0400	0.0740	0.0400	0.0804

		MSE	Time	MSE	Time	MSE	Time	MSE	Time
Method		$N = 100, 000$		$N = 200, 000$		$N = 400, 000$		$N = 800, 000$
OSMAC		0.0460	0.0213	0.0460	0.0410	0.0452	0.0875	0.0476	0.1679
SOS	$K = 1$	0.2346	0.0058	0.2320	0.0070	0.2300	0.0064	0.2274	0.0086
	$K = 5$	0.0415	0.0101	0.0419	0.0090	0.0414	0.0124	0.0418	0.0150
	$K = 10$	0.0410	0.0171	0.0405	0.0168	0.0419	0.0175	0.0406	0.0256
	$K = 20$	0.0404	0.0318	0.0409	0.0322	0.0405	0.0378	0.0403	0.0437
	$K = 40$	0.0401	0.0574	0.0398	0.0618	0.0400	0.0740	0.0400	0.0804

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