A New Conway Maxwell–Poisson Liu Regression Estimator—Method and Application

Table 2

Estimated MSEs of the COMPRM estimators, when and .

n	ρ	MLE
50	0.80	2.684	1.040	1.180	1.784	0.799	0.969	1.037	1.492	0.783	0.969	0.947	1.492	0.768
	0.90	4.934	1.608	1.901	3.281	0.969	1.335	1.377	2.469	0.872	1.199	1.081	2.469	0.778
	0.95	9.097	2.605	2.899	6.515	1.004	1.745	1.752	4.216	0.670	1.420	1.142	4.216	0.594
	0.99	47.502	14.233	16.051	40.438	4.406	6.521	6.897	20.815	0.493	4.362	3.026	20.815	0.416
100	0.80	2.394	0.804	0.899	1.425	0.678	0.773	0.820	1.249	0.674	0.803	0.774	1.249	0.673
	0.90	4.481	1.225	1.330	2.696	0.695	1.010	1.014	2.001	0.645	0.952	0.844	2.001	0.611
	0.95	9.434	2.326	2.676	6.488	0.709	1.721	1.698	4.536	0.568	1.452	1.145	4.536	0.520
	0.99	42.393	10.322	11.148	34.381	2.227	5.431	5.614	18.029	0.316	3.627	2.361	18.029	0.275
150	0.80	2.265	0.759	0.884	1.287	0.653	0.732	0.805	1.158	0.648	0.758	0.740	1.158	0.644
	0.90	3.848	1.082	1.210	2.182	0.705	0.917	0.949	1.787	0.626	0.847	0.759	1.787	0.569
	0.95	7.873	1.634	1.871	4.965	0.652	1.345	1.33	3.485	0.565	1.220	0.992	3.485	0.537
	0.99	36.490	7.785	8.920	27.795	1.090	4.670	4.898	15.420	0.278	3.248	2.237	15.420	0.258
200	0.80	2.259	0.735	0.820	1.244	0.631	0.715	0.763	1.120	0.628	0.744	0.728	1.120	0.626
	0.90	4.343	1.058	1.244	2.572	0.674	0.978	1.029	2.129	0.638	0.970	0.853	2.129	0.620
	0.95	8.051	1.579	1.834	4.819	0.671	1.326	1.310	3.490	0.528	1.235	1.026	3.490	0.500
	0.99	37.898	8.962	9.045	30.023	1.677	4.887	5.054	16.270	0.261	3.286	2.172	16.270	0.231