Table 2 Parameter estimates and their mean squared errors.

From: Some developments on seasonal INAR processes with application to influenza data

n	\(\hat{\lambda }_{ YW}\)	\(\hat{\alpha }_{ YW}\)	\(\hat{\delta }_{{ YW}}\)	\(\hat{\lambda }_{{ CLS}}\)	\(\hat{\alpha }_{{ CLS}}\)	\(\hat{\delta }_{ CLS}\)	\(\hat{\lambda }_{{ CML}}\)	\(\hat{\alpha }_{{ CML}}\)	\(\hat{\delta }_{{ CML}}\)
\(\lambda =0.3\), \(\alpha =0.5\), \(\delta =0.7\)
100	0.2739	0.6347	0.6307	0.2747	0.5749	0.6324	0.2952	0.5234	0.6919
100	0.0157	0.0799	0.0205	0.0133	0.0159	0.0259	0.0081	0.0164	0.0054
300	0.2954	0.5363	0.6827	0.2947	0.5504	0.6641	0.2963	0.5073	0.6956
300	0.0046	0.0083	0.0042	0.0046	0.0072	0.0077	0.0022	0.0036	0.0014
500	0.2953	0.5229	0.6868	0.2954	0.5396	0.6721	0.2993	0.5088	0.6963
500	0.0029	0.0043	0.0026	0.0026	0.0046	0.0038	0.0013	0.0022	0.0009
1000	0.2966	0.5142	0.6919	0.2970	0.5303	0.6785	0.3005	0.5025	0.6998
1000	0.0013	0.0021	0.0012	0.0012	0.0029	0.0019	0.0006	0.0011	0.0004
\(\lambda =0.5\), \(\alpha =1\), \(\delta =0.5\)
100	0.4319	1.1772	0.3866	0.4656	1.0993	0.4106	0.4838	1.0845	0.4713
100	0.0198	0.4565	0.0848	0.0177	0.0358	0.0545	0.0138	0.0986	0.0190
300	0.4756	1.0852	0.4525	0.4864	1.0718	0.4472	0.4912	1.0121	0.4920
300	0.0054	0.0560	0.0168	0.0050	0.0160	0.0147	0.0040	0.0209	0.0054
500	0.4865	1.0564	0.4689	0.4905	1.0668	0.4505	0.4954	1.0179	0.4905
500	0.0032	0.0282	0.0097	0.0030	0.0114	0.0099	0.0022	0.0115	0.0028
1000	0.4928	1.0226	0.4867	0.4930	1.0622	0.4576	0.4988	1.0087	0.4973
1000	0.0015	0.0116	0.0043	0.0014	0.0089	0.0058	0.0011	0.0062	0.0014
\(\lambda =0.6\), \(\alpha =1.5\), \(\delta =-0.5\)
100	0.5188	1.4169	\(-0.4893\)	0.5621	1.4184	\(-0.4468\)	0.5688	1.5475	\(-0.5601\)
100	0.0172	0.9482	0.5433	0.0150	0.0833	0.0372	0.0124	0.1645	0.0796
300	0.5784	1.5685	\(-0.5892\)	0.5838	1.4341	\(-0.4443\)	0.5919	1.5326	\(-0.5312\)
300	0.0043	0.4783	0.2972	0.0039	0.0382	0.0268	0.0028	0.0712	0.0354
500	0.5851	1.5689	\(-0.5748\)	0.5917	1.4460	\(-0.4464\)	0.5943	1.5242	\(-0.5266\)
500	0.0026	0.2143	0.1535	0.0023	0.0261	0.0188	0.0017	0.0478	0.0239
1000	0.5936	1.5606	\(-0.5558\)	0.5961	1.4491	\(-0.4494\)	0.5971	1.5090	\(-0.5107\)
1000	0.0011	0.0649	0.0558	0.0011	0.0130	0.0130	0.0010	0.0225	0.0109
\(\lambda =0.7\), \(\alpha =2\), \(\delta =-1\)
100	0.5804	1.4330	\(-0.5686\)	0.6590	1.8734	\(-0.9271\)	0.6653	1.9439	\(-0.9821\)
100	0.0240	1.8765	1.2004	0.0153	0.1675	0.0744	0.0122	0.3010	0.1417
300	0.6713	1.7131	\(-0.8222\)	0.6858	1.8965	\(-0.9120\)	0.6891	2.0113	\(-1.0204\)
300	0.0045	1.7318	1.0939	0.0038	0.0983	0.0559	0.0025	0.1510	0.0841
500	0.6848	1.8997	\(-0.9649\)	0.6906	1.9002	\(-0.9101\)	0.6936	2.0205	\(-1.0274\)
500	0.0022	1.2074	0.8342	0.0022	0.0732	0.0491	0.0015	0.1214	0.0667
1000	0.6930	2.0364	\(-1.0658\)	0.6932	1.9088	\(-0.9172\)	0.6982	2.0339	\(-1.0286\)
1000	0.0011	0.7614	0.5599	0.0010	0.0393	0.0289	0.0007	0.0678	0.0378

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Table 2 Parameter estimates and their mean squared errors.

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