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

Table 5

Estimated MSEs of the COMPRM estimators, when and .

n	ρ	MLE
50	0.80	9.425	2.728	3.368	6.848	1.594	2.463	2.673	5.631	1.552	2.245	1.963	5.631	1.519
	0.90	15.890	3.527	4.736	11.296	1.754	2.986	2.869	8.429	1.382	2.416	1.846	8.429	1.282
	0.95	31.650	8.583	10.852	24.145	2.185	5.246	5.609	16.372	1.186	3.324	2.333	16.372	1.016
	0.99	166.316	45.334	53.332	144.637	3.647	17.780	17.299	78.352	0.821	10.209	5.272	78.352	0.660
100	0.80	5.473	1.526	1.704	3.380	1.370	1.511	1.605	3.035	1.368	1.569	1.476	3.035	1.357
	0.90	10.829	2.108	2.581	7.114	1.281	2.043	2.027	6.110	1.277	1.954	1.537	6.110	1.266
	0.95	22.691	4.590	6.081	15.722	1.324	3.803	4.037	12.235	1.175	2.898	2.102	12.235	1.106
	0.99	100.391	18.915	22.790	78.922	1.462	11.449	10.651	52.215	0.510	6.570	3.172	52.215	0.453
150	0.80	5.143	1.415	1.588	3.117	1.317	1.409	1.502	2.878	1.317	1.499	1.408	2.878	1.317
	0.90	9.447	1.893	2.267	5.963	1.302	1.831	1.813	5.185	1.302	1.850	1.493	5.185	1.295
	0.95	18.639	3.337	3.780	12.276	1.069	2.831	2.625	9.948	1.024	2.347	1.569	9.948	0.990
	0.99	84.698	13.362	14.388	65.071	1.294	8.650	7.444	42.010	0.420	5.191	2.458	42.010	0.383
200	0.80	4.999	1.406	1.533	2.975	1.344	1.402	1.471	2.761	1.344	1.478	1.407	2.761	1.344
	0.90	8.705	1.643	2.148	5.186	1.226	1.673	1.756	4.661	1.226	1.714	1.443	4.661	1.226
	0.95	16.303	2.517	3.159	10.367	1.080	2.465	2.337	8.555	1.049	2.214	1.560	8.555	1.040
	0.99	81.821	14.189	15.855	62.200	1.147	9.865	9.359	43.014	0.442	5.535	2.729	43.014	0.403