Table 4.

Coverage probabilities of the 95% confidence interval for the optimal cut-point c under the mixture model.

n1	n2	J	${\sigma }_1^2$
			0.5	1	3	5
10	10	0.2	0.9510	0.9355	0.9640	0.9695
		0.4	0.9535	0.9540	0.9590	0.9575
		0.6	0.9670	0.9655	0.9510	0.9515
		0.8	0.9580	0.9615	0.9585	0.9565
		0.9	0.9415	0.9470	0.9450	0.9465
20	20	0.2	0.9420	0.9125	0.9410	0.9455
		0.4	0.9455	0.9340	0.9355	0.9315
		0.6	0.9535	0.9550	0.9415	0.9335
		0.8	0.9530	0.9535	0.9470	0.9405
		0.9	0.9345	0.9330	0.9320	0.9290
10	30	0.2	0.9410	0.9200	0.9435	0.9500
		0.4	0.9455	0.9400	0.9295	0.9335
		0.6	0.9570	0.9575	0.9485	0.9415
		0.8	0.9475	0.9495	0.9430	0.9415
		0.9	0.9420	0.9385	0.9350	0.9290
50	50	0.2	0.9255	0.8870	0.9465	0.9460
		0.4	0.9170	0.9135	0.9365	0.9350
		0.6	0.9395	0.9515	0.9505	0.9350
		0.8	0.9445	0.9455	0.9415	0.9360
		0.9	0.9260	0.9280	0.9265	0.9245
100	100	0.2	0.9150	0.8855	0.9250	0.9250
		0.4	0.9150	0.9120	0.9145	0.9185
		0.6	0.9400	0.9440	0.9385	0.9250
		0.8	0.9335	0.9425	0.9330	0.9275
		0.9	0.9270	0.9345	0.9275	0.9220

The mixture model is defined as $Y_i~(1-B_i)N({\mu }_i,\frac{1}{1.1}{\sigma }_i^2)+B_{i}N({\mu }_i,\frac{2}{1.1}{\sigma }_i^2)$ where B_i ~ Bernoulli(0.1) for i = 1, 2.