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Table 4 Maximum posterior classification (MAP) for men, J = 7

From: A nonparametric random coefficient approach for life expectancy growth using a hierarchical mixture likelihood model with application to regional data from North Rhine-Westphalia (Germany)

Region Name Class e .1 e .2 e .3 e .4 e .5 e .6 e .7
1 Düsseldorf 2 0 0.95 0.05 0 0 0 0
2 Duisburg 1 1 0 0 0 0 0 0
3 Essen 2 0 1 0 0 0 0 0
4 Krefeld 4 0 0 0 1 0 0 0
5 Mönchengladbach 2 0 1 0 0 0 0 0
6 Mülheim a.d. Ruhr 4 0 0 0.01 0.99 0 0 0
7 Oberhausen 1 1 0 0 0 0 0 0
8 Remscheid 2 0 1 0 0 0 0 0
9 Solingen 4 0 0 0 1 0 0 0
10 Wuppertal 3 0 0 1 0 0 0 0
11 Kleve 3 0 0 1 0 0 0 0
12 Mettmann 6 0 0 0 0 0.03 0.97 0
13 Neuss 6 0 0 0 0 0.01 0.99 0
14 Viersen 4 0 0 0 1 0 0 0
15 Wesel 4 0 0 0 1 0 0 0
16 Aachen (city) 6 0 0 0 0 0 1 0
17 Bonn 7 0 0 0 0 0 0 1
18 Köln 4 0 0 0.03 0.97 0 0 0
19 Leverkusen 5 0 0 0 0 1 0 0
20 Aachen (rural) 4 0 0 0.16 0.84 0 0 0
21 Düren 4 0 0 0 1 0 0 0
22 Erftkreis 5 0 0 0 0 1 0 0
23 Euskirchen 3 0 0 1 0 0 0 0
24 Heinsberg 4 0 0 0 1 0 0 0
25 Oberbergischer Kreis 4 0 0 0 1 0 0 0
26 Rheinisch-Bergischer Kreis 7 0 0 0 0 0 0 1
27 Rhein-Sieg-Kreis 7 0 0 0 0 0 0 1
28 Bottrop 2 0 1 0 0 0 0 0
29 Gelsenkirchen 1 1 0 0 0 0 0 0
30 Münster 7 0 0 0 0 0 0 1
31 Borken 4 0 0 0 0.96 0.04 0 0
32 Coesfeld 6 0 0 0 0 0.14 0.86 0
33 Recklinghausen 2 0 1 0 0 0 0 0
34 Steinfurt 5 0 0 0 0 1 0 0
35 Warendorf 5 0 0 0 0 0.96 0.04 0
36 Bielefeld 5 0 0 0 0 1 0 0
37 Gütersloh 6 0 0 0 0 0 1 0
38 Herford 5 0 0 0 0 1 0 0
39 Höxter 5 0 0 0 0 0.99 0.01 0
40 Lippe 5 0 0 0 0 0.92 0.08 0
41 Minden-Lübbecke 4 0 0 0.01 0.99 0 0 0
42 Paderborn 5 0 0 0 0 1 0 0
43 Bochum 2 0 1 0 0 0 0 0
44 Dortmund 2 0 1 0 0 0 0 0
45 Hagen 2 0 1 0 0 0 0 0
46 Hamm 3 0 0 1 0 0 0 0
47 Herne 2 0 1 0 0 0 0 0
48 Ennepe-Ruhr-Kreis 3 0 0 1 0 0 0 0
49 Hochsauerlandkreis 4 0 0 0 0.88 0.12 0 0
50 Märkischer Kreis 3 0 0 1 0 0 0 0
51 Olpe 4 0 0 0 1 0 0 0
52 Siegen-Wittgenstein 4 0 0 0 1 0 0 0
53 Soest 3 0 0 1 0 0 0 0
54 Unna 3 0 0 0.99 0.01 0 0 0