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

Table 12

Estimated MSEs of the COMPRM estimators, when and .

n	ρ	MLE
50	0.80	63.168	16.301	22.056	54.539	2.494	10.382	9.771	39.565	2.417	6.187	3.966	39.565	2.400
	0.90	118.398	30.318	41.830	102.978	1.871	15.609	12.943	68.747	1.669	7.711	3.732	68.747	1.617
	0.95	238.346	67.567	87.356	213.248	2.992	28.167	25.623	137.368	1.396	11.737	5.443	137.368	1.177
	0.99	1344.30	431.449	542.212	1268.88	12.429	163.063	144.474	835.436	0.996	51.262	13.749	835.436	0.787
100	0.80	25.804	4.901	6.484	19.299	2.149	4.496	4.241	15.238	2.146	3.671	2.748	15.238	2.143
	0.90	52.363	10.086	13.886	40.252	1.616	7.514	6.937	30.561	1.599	4.650	2.894	30.561	1.585
	0.95	96.244	18.678	21.891	76.807	1.200	10.793	8.237	54.968	1.105	5.701	2.632	54.968	1.064
	0.99	481.989	101.187	120.566	427.314	1.354	45.019	37.943	272.905	0.464	17.306	6.364	272.905	0.412
150	0.80	22.659	3.878	4.627	16.281	2.041	3.716	3.463	13.5085	2.038	3.268	2.466	13.508	2.036
	0.90	40.844	6.258	7.857	29.751	1.606	5.289	4.425	23.590	1.591	3.759	2.400	23.590	1.580
	0.95	85.259	14.897	17.858	65.843	1.049	10.007	8.317	49.744	1.017	5.300	2.540	49.744	0.998
	0.99	411.938	73.949	80.989	347.646	1.932	40.042	30.879	235.329	0.459	16.923	5.653	235.329	0.383
200	0.80	21.622	3.446	4.326	15.123	2.175	3.564	3.413	13.061	2.175	3.325	2.571	13.061	2.175
	0.90	39.402	6.077	7.915	28.104	1.542	5.388	4.724	23.105	1.540	3.866	2.468	23.105	1.539
	0.95	72.769	10.799	13.576	54.355	1.100	8.425	6.998	41.265	1.067	4.919	2.501	41.265	1.051
	0.99	366.248	60.925	68.981	311.750	0.510	35.042	30.722	205.912	0.376	14.729	6.062	205.912	0.340