Table 2.

360 (n = 100) and 105 (n = 200) Monte Carlo samples that converge under the EE method when ρ = 1. The estimated standard deviation of the EM algorithms is listed on the third column and the corresponding Monte Carlo standard deviation of both estimates are listed in column 4. The last column gives the MSE ratio of EE/MM and EE/PHF

		True	Estimation	Est SD	MC SD	MSE	MSE ratio
n = 100	β(MM)	−1.3	−1.3994	0.5745	0.5737	0.3429	141.03%
	(PHF)	−1.3	−1.5150	0.5655	0.5700	0.3711	130.32%
	(EE)	−1.3	−1.6066		0.6242	0.4836
	α1(MM)	−0.12	−0.1808	0.3065	0.2804	0.0823	102.43%
	(PHF)	−0.12	−0.1318	0.3160	0.3124	0.0977	86.28%
	(EE)	−0.12	−0.1862		0.2827	0.0843
	α2(MM)	0.56	0.5076	0.4510	0.4353	0.1922	100.10%
	(PHF)	0.56	0.6096	0.4852	0.4588	0.2130	110.65%
	(EE)	0.56	0.5284		0.4375	0.1924
n = 200	β(MM)	−1.3	−1.3376	0.3967	0.4104	0.1699	119.54%
	(PHF)	−1.3	−1.3933	0.3993	0.4035	0.1715	118.43%
	(EE)	−1.3	−1.4575		0.4222	0.2031
	α1(MM)	−0.12	−0.1544	0.2174	0.2094	0.0450	103.11%
	(PHF)	−0.12	−0.1217	0.2226	0.2218	0.0492	94.31%
	(EE)	−0.12	−0.1562		0.2123	0.0464
	α2(MM)	0.56	0.5076	0.3210	0.3242	0.1079	101.85%
	(PHF)	0.56	0.5798	0.3408	0.3436	0.1185	92.74%
	(EE)	0.56	0.5270		0.3299	0.1099