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

Table 8

Estimated MSEs of the COMPRM estimators, when and .

n	ρ	MLE
50	0.80	17.974	4.025	5.120	13.518	2.213	3.730	3.511	10.519	2.180	3.262	2.658	10.519	2.172
	0.90	34.060	8.389	10.819	26.731	1.919	5.838	5.447	18.093	1.890	4.207	3.006	18.093	1.880
	0.95	54.728	11.931	15.096	42.774	1.507	6.154	5.245	26.395	1.368	4.145	2.618	26.395	1.337
	0.99	316.415	88.845	112.764	281.113	7.263	33.531	30.884	161.060	0.954	15.262	7.057	161.060	0.777
100	0.80	11.067	2.437	2.848	7.294	2.057	2.489	2.560	6.404	2.057	2.541	2.253	6.404	2.057
	0.90	19.801	3.296	4.069	13.370	1.730	3.111	2.924	11.072	1.730	2.812	2.137	11.072	1.730
	0.95	38.528	6.427	7.889	27.660	1.373	4.987	4.291	21.080	1.360	3.560	2.235	21.080	1.333
	0.99	185.349	29.841	35.463	150.382	1.018	18.001	15.031	97.777	0.536	8.956	3.796	97.777	0.505
150	0.80	12.028	2.470	2.948	7.802	2.055	2.516	2.597	6.946	2.055	2.575	2.261	6.946	2.055
	0.90	21.884	3.264	3.796	14.966	1.677	3.097	2.754	12.285	1.677	2.845	2.038	12.285	1.677
	0.95	42.069	5.995	8.037	29.204	1.293	5.274	4.610	22.413	1.203	3.671	2.241	22.413	1.175
	0.99	208.743	34.159	37.745	166.938	0.857	20.269	16.772	107.252	0.444	10.152	4.436	107.255	0.412
200	0.80	10.360	2.215	2.467	6.557	1.960	2.234	2.294	5.851	1.960	2.336	2.104	5.851	1.960
	0.90	20.889	2.940	3.819	13.623	1.692	3.073	2.909	11.585	1.692	2.892	2.164	11.585	1.692
	0.95	40.342	5.571	7.000	27.612	1.203	4.927	4.357	21.660	1.201	3.613	2.153	21.660	1.199
	0.99	197.037	31.122	33.893	157.644	0.600	18.719	15.572	100.621	0.438	9.806	4.221	100.621	0.415