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

Table 6

Estimated MSEs of the COMPRM estimators, when and .

n	ρ	MLE
50	0.80	14.473	4.064	5.275	11.100	1.502	3.347	3.550	8.483	1.382	2.633	2.123	8.483	1.300
	0.90	25.716	6.443	8.688	19.558	1.707	4.406	4.017	14.512	1.290	2.917	1.972	14.512	1.047
	0.95	45.979	12.663	16.753	36.195	2.073	6.835	7.173	23.605	0.963	4.137	2.800	23.605	0.779
	0.99	239.991	70.108	90.555	210.552	6.096	27.822	27.705	123.009	0.632	13.880	6.909	123.009	0.475
100	0.80	9.264	2.142	2.673	6.305	1.251	2.029	2.073	5.458	1.214	1.904	1.539	5.458	1.190
	0.90	17.536	3.760	4.909	12.580	1.178	3.268	3.273	10.155	1.102	2.529	1.812	10.155	1.018
	0.95	32.503	6.932	8.797	23.906	1.142	4.849	4.371	18.245	0.820	3.153	1.835	18.245	0.720
	0.99	154.742	34.669	39.781	131.302	2.135	18.364	19.124	81.775	0.382	9.801	5.474	81.775	0.305
150	0.80	7.998	1.746	2.195	5.146	1.129	1.676	1.774	4.570	1.128	1.651	1.378	4.570	1.122
	0.90	13.893	2.699	3.387	9.098	0.933	2.298	2.274	7.340	0.903	1.937	1.408	7.340	0.861
	0.95	27.333	5.206	6.568	18.870	0.821	3.825	3.745	14.926	0.695	2.599	1.580	14.926	0.650
	0.99	131.159	28.549	31.278	107.210	3.171	15.696	15.732	68.035	0.338	8.194	4.451	68.035	0.278
200	0.80	8.005	1.759	2.149	5.127	1.144	1.718	1.847	4.614	1.144	1.679	1.407	4.614	1.144
	0.90	13.079	2.396	2.965	8.504	0.938	2.140	2.154	7.153	0.909	1.837	1.355	7.153	0.882
	0.95	25.653	4.540	5.830	17.244	0.887	3.706	3.534	13.661	0.711	2.618	1.579	13.661	0.658
	0.99	126.066	25.055	26.065	104.324	1.750	14.312	12.421	67.811	0.332	7.697	3.384	67.811	0.280