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

Table 9

Estimated MSEs of the COMPRM estimators, when and .

n	ρ	MLE
50	0.80	27.903	7.425	9.269	22.411	2.176	5.453	5.337	16.601	1.973	3.954	3.021	16.601	1.855
	0.90	51.725	13.796	18.602	42.651	1.848	8.369	7.529	29.956	1.614	4.856	2.976	29.956	1.544
	0.95	102.989	30.676	37.501	86.841	2.264	13.523	11.499	57.770	1.333	6.594	3.325	57.770	1.150
	0.99	445.906	134.994	165.890	409.512	5.829	48.780	45.641	237.160	0.757	19.410	7.605	237.160	0.608
100	0.80	16.870	3.345	4.217	12.011	1.663	3.129	3.119	10.063	1.663	2.729	2.116	10.063	1.663
	0.90	31.019	6.091	7.756	22.441	1.404	4.892	4.507	17.638	1.332	3.375	2.171	17.638	1.266
	0.95	60.990	11.785	14.510	46.411	1.026	7.977	7.073	35.055	0.986	4.501	2.462	35.055	0.928
	0.99	288.615	62.229	72.243	251.761	0.993	30.630	27.538	161.408	0.478	13.377	5.715	161.408	0.391
150	0.80	18.354	3.189	3.954	12.846	1.532	3.020	2.901	10.840	1.532	2.643	1.971	10.840	1.532
	0.90	36.496	5.911	7.624	26.979	1.167	4.998	4.403	21.052	1.156	3.534	2.140	21.052	1.146
	0.95	66.723	11.833	14.288	49.331	0.850	7.902	6.656	36.398	0.749	4.603	2.266	36.398	0.729
	0.99	327.512	66.195	72.311	282.633	2.427	33.383	27.859	176.462	0.349	15.224	6.009	176.462	0.286
200	0.80	15.941	2.708	3.502	10.581	1.453	2.675	2.669	9.089	1.453	2.450	1.885	9.089	1.453
	0.90	28.468	4.451	5.415	19.906	1.093	3.743	3.469	15.345	1.080	2.891	1.900	15.345	1.077
	0.95	60.939	10.047	10.844	44.947	0.945	7.012	5.101	34.236	0.774	4.088	1.794	34.236	0.746
	0.99	305.753	58.878	61.435	263.551	3.756	31.143	27.053	163.060	0.315	14.384	6.515	163.060	0.265