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

Table 1

Estimated MSEs of the COMPRM estimators, when and .

n	ρ	MLE
50	0.80	1.916	0.917	1.039	1.385	0.766	0.892	0.960	1.239	0.763	0.876	0.862	1.239	0.752
	0.90	3.466	1.242	1.400	2.168	0.947	1.138	1.195	1.814	0.889	1.094	1.046	1.814	0.848
	0.95	7.280	2.186	2.494	4.986	1.079	1.708	1.790	3.626	0.896	1.546	1.372	3.626	0.844
	0.99	35.927	9.957	11.894	29.365	2.263	4.737	4.650	15.357	0.584	3.565	2.589	15.357	0.493
100	0.80	1.909	0.858	0.911	1.245	0.747	0.831	0.866	1.141	0.747	0.841	0.827	1.141	0.746
	0.90	3.402	1.071	1.199	2.050	0.840	1.012	1.048	1.755	0.803	0.994	0.923	1.755	0.778
	0.95	6.628	1.723	2.004	4.160	0.946	1.517	1.576	3.265	0.797	1.376	1.185	3.265	0.756
	0.99	28.597	6.233	7.098	21.400	1.279	3.610	3.525	11.931	0.381	2.697	1.876	11.931	0.342
150	0.80	1.572	0.684	0.733	0.969	0.661	0.677	0.717	0.917	0.661	0.707	0.703	0.917	0.661
	0.90	3.027	0.976	1.081	1.765	0.793	0.954	0.989	1.590	0.793	0.966	0.910	1.590	0.786
	0.95	6.304	1.492	1.712	3.801	0.841	1.412	1.471	3.178	0.777	1.333	1.140	3.178	0.754
	0.99	30.140	5.822	6.945	22.575	0.812	4.349	4.477	14.057	0.383	3.240	2.303	14.057	0.364
200	0.80	1.653	0.705	0.767	0.987	0.681	0.706	0.746	0.943	0.679	0.729	0.724	0.943	0.677
	0.90	3.097	0.890	1.033	1.769	0.768	0.882	0.971	1.574	0.768	0.948	0.919	1.574	0.762
	0.95	6.031	1.344	1.526	3.558	0.807	1.287	1.338	2.917	0.758	1.296	1.147	2.917	0.740
	0.99	27.140	5.716	6.299	20.627	1.365	3.945	4.174	12.382	0.360	2.873	2.073	12.384	0.331