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

Table 3

Estimated MSEs of the COMPRM estimators, when and .

n	ρ	MLE
50	0.80	4.248	1.473	1.807	2.962	0.863	1.324	1.419	2.399	0.833	1.237	1.153	2.399	0.780
	0.90	8.065	2.691	3.249	5.903	1.113	1.925	2.011	4.200	0.796	1.547	1.296	4.200	0.660
	0.95	14.120	4.436	5.285	10.574	1.480	2.551	2.663	6.468	0.619	1.901	1.542	6.468	0.482
	0.99	76.540	28.089	32.037	68.443	7.498	10.843	11.160	36.549	0.524	6.866	4.431	36.549	0.388
100	0.80	3.593	1.075	1.222	2.209	0.690	0.923	0.964	1.756	0.651	0.895	0.818	1.756	0.615
	0.90	5.714	1.427	1.726	3.595	0.664	1.116	1.142	2.510	0.517	0.964	0.810	2.510	0.481
	0.95	13.176	3.827	4.063	9.600	1.282	2.187	2.082	6.052	0.558	1.627	1.166	6.052	0.443
	0.99	63.875	16.989	18.711	54.433	3.090	7.656	7.821	26.986	0.276	5.326	3.669	26.986	0.233
150	0.80	3.459	1.012	1.228	2.103	0.650	0.924	0.988	1.782	0.621	0.904	0.833	1.782	0.601
	0.90	6.255	1.539	1.798	3.804	0.667	1.230	1.229	2.914	0.557	1.080	0.889	2.914	0.504
	0.95	10.826	2.301	2.784	7.027	0.637	1.537	1.540	4.342	0.386	1.282	1.021	4.342	0.347
	0.99	66.987	19.140	20.367	56.520	5.480	9.808	10.737	32.632	0.291	6.243	4.201	32.632	0.241
200	0.80	3.470	0.998	1.136	2.078	0.593	0.887	0.915	1.789	0.574	0.861	0.768	1.789	0.561
	0.90	6.122	1.544	1.731	3.897	0.690	1.278	1.286	3.007	0.549	1.132	0.965	3.007	0.495
	0.95	11.942	2.619	2.988	8.289	0.649	1.773	1.758	5.212	0.405	1.481	1.161	5.212	0.366
	0.99	57.475	14.779	15.646	46.692	2.905	6.891	6.849	24.849	0.257	4.903	3.352	24.849	0.226