Table 3(b).

Coverage Probability of 95% Confidence Interval for AAPC over [c, d]: Two joinpoint model case

n	τ1, τ2	σ	[c, d]	mCCI	MCI	FLT	EmpQ	n	τ1, τ2	σ	[c, d]	mCCI	MCI	FLT	EmpQ
20	13,17	0.01	[1, 20]	0.999	0.886	0.918	0.964	40	33, 37	0.01	[1, 40]	0.999	0.870	0.881	0.972
			[11, 20]	0.994	0.874	0.899	0.942				[31, 40]	0.993	0.884	0.898	0.966
			[16, 20]	0.898	0.840	0.860	0.918				[36, 40]	0.897	0.861	0.870	0.946
		0.05	[1, 20]	1.000	0.845	0.886	0.983			0.05	[1, 40]	1.000	0.876	0.889	0.986
			[11, 20]	0.948	0.849	0.872	0.950				[31, 40]	0.966	0.883	0.895	0.972
			[16, 20]	0.848	0.754	0.802	0.954				[36, 40]	0.854	0.799	0.812	0.965
		0.1	[1, 20]	1.000	0.802	0.850	0.971			0.1	[1, 40]	1.000	0.859	0.869	0.981
			[11, 20]	0.926	0.826	0.867	0.966				[31, 40]	0.829	0.764	0.777	0.961
			[16, 20]	0.790	0.699	0.742	0.961				[36, 40]	0.740	0.683	0.690	0.965
	11, 17	0.01	[1, 20]	0.999	0.892	0.920	0.958		31, 37	0.01	[1, 40]	0.996	0.887	0.901	0.963
			[11, 20]	0.977	0.867	0.896	0.927				[31, 40]	0.975	0.871	0.886	0.942
			[16, 20]	0.916	0.852	0.880	0.916				[36, 40]	0.902	0.865	0.871	0.924
		0.05	[1, 20]	0.999	0.847	0.887	0.979			0.05	[1, 40]	1.000	0.867	0.884	0.989
			[11, 20]	0.920	0.826	0.857	0.939				[31, 40]	0.946	0.868	0.881	0.963
			[16, 20]	0.817	0.702	0.763	0.955				[36, 40]	0.830	0.774	0.786	0.970
		0.1	[1, 20]	1.000	0.810	0.855	0.975			0.1	[1, 40]	1.000	0.863	0.871	0.987
			[11, 20]	0.909	0.824	0.860	0.961				[31, 40]	0.847	0.790	0.802	0.942
			[16, 20]	0.818	0.713	0.772	0.966				[36, 40]	0.781	0.715	0.733	0.973
	5, 17	0.01	[1, 20]	1.000	0.906	0.940	0.961		20, 37	0.01	[1, 40]	0.997	0.923	0.932	0.960
			[11, 20]	0.992	0.898	0.926	0.943				[31, 40]	0.990	0.935	0.939	0.956
			[16, 20]	0.950	0.884	0.906	0.928				[36, 40]	0.961	0.910	0.917	0.961
		0.05	[1, 20]	0.998	0.819	0.863	0.961			0.05	[1, 40]	1.000	0.818	0.838	0.984
			[11, 20]	0.901	0.790	0.838	0.939				[31, 40]	0.723	0.634	0.662	0.969
			[16, 20]	0.670	0.572	0.618	0.934				[36, 40]	0.450	0.394	0.399	0.932
		0.1	[1, 20]	0.999	0.778	0.836	0.955			0.1	[1, 40]	1.000	0.851	0.865	0.991
			[11, 20]	0.931	0.804	0.859	0.948				[31, 40]	0.795	0.710	0.724	0.974
			[16, 20]	0.812	0.686	0.758	0.951				[36, 40]	0.534	0.464	0.482	0.936
	5, 13	0.01	[1, 20]	1.000	0.912	0.944	0.944		20, 33	0.01	[1, 40]	1.000	0.934	0.944	0.963
			[11, 20]	0.985	0.929	0.951	0.951				[31, 40]	0.998	0.954	0.963	0.973
			[16, 20]	0.941	0.912	0.938	0.938				[36, 40]	0.941	0.932	0.941	0.974
		0.05	[1, 20]	0.999	0.858	0.897	0.971			0.05	[1, 40]	1.000	0.864	0.873	0.985
			[11, 20]	0.930	0.831	0.870	0.949				[31, 40]	0.790	0.731	0.739	0.957
			[16, 20]	0.840	0.742	0.780	0.957				[36, 40]	0.721	0.665	0.673	0.965
		0.1	[1, 20]	0.999	0.806	0.859	0.960			0.1	[1, 40]	1.000	0.863	0.877	0.995
			[11, 20]	0.934	0.827	0.872	0.957				[31, 40]	0.750	0.672	0.693	0.971
			[16, 20]	0.874	0.761	0.820	0.963				[36, 40]	0.649	0.571	0.593	0.965
	5, 11	0.01	[1, 20]	1.000	0.921	0.955	0.969		20, 31	0.01	[1, 40]	1.000	0.931	0.944	0.961
			[11, 20]	0.952	0.913	0.928	0.924				[31, 40]	0.959	0.933	0.940	0.950
			[16, 20]	0.939	0.916	0.934	0.962				[36, 40]	0.940	0.933	0.940	0.967
		0.05	[1, 20]	0.999	0.864	0.912	0.974			0.05	[1, 40]	1.000	0.875	0.884	0.988
			[11, 20]	0.916	0.820	0.862	0.952				[31, 40]	0.806	0.765	0.776	0.950
			[16, 20]	0.896	0.790	0.834	0.959				[36, 40]	0.798	0.742	0.751	0.974
		0.1	[1, 20]	0.999	0.812	0.866	0.962			0.1	[1, 40]	1.000	0.862	0.877	0.994
			[11, 20]	0.930	0.833	0.867	0.954				[31, 40]	0.762	0.682	0.702	0.962
			[16, 20]	0.910	0.804	0.852	0.963				[36, 40]	0.756	0.672	0.698	0.969
	4, 8	0.01	[1, 20]	0.999	0.897	0.927	0.966		10, 20	0.01	[1, 40]	1.000	0.941	0.956	0.964
			[11, 20]	0.936	0.899	0.923	0.957				[31, 40]	0.941	0.936	0.941	0.963
			[16, 20]	0.934	0.888	0.918	0.959				[36, 40]	0.941	0.936	0.941	0.963
		0.05	[1, 20]	0.998	0.852	0.890	0.964			0.05	[1, 40]	1.000	0.901	0.910	0.991
			[11, 20]	0.948	0.842	0.887	0.948				[31, 40]	0.895	0.825	0.843	0.982
			[16, 20]	0.930	0.822	0.869	0.958				[36, 40]	0.895	0.833	0.855	0.982
		0.1	[1, 20]	0.999	0.808	0.859	0.955			0.1	[1, 40]	1.000	0.882	0.898	0.991
			[11, 20]	0.944	0.827	0.879	0.949				[31, 40]	0.854	0.753	0.777	0.990
			[16, 20]	0.921	0.812	0.862	0.961				[36, 40]	0.852	0.773	0.793	0.989

• Model: y_i = β₀ + β₁x_i + δ₁(x_i − τ₁)⁺ + δ₂(x − τ₂)⁺ + ε_i, (i = 1,…, n), where a⁺ = max(0; a) and ε_i ~ N(0; σ₂).

• $(AP{C}_1,AP{C}_2,AP{C}_3)=\left((e^{{\beta }_1}-1)\times 100,(e^{{\beta }_2}-1)\times 100,(e^{{\beta }_3}-1)\times 100\right)=(1.5,-1,-2.5)$