| |
|
|
|
NNTS test |
|
|
|
| Model |
n1 |
n2 |
α |
M0 = 1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
Watson |
Rao Mean |
Rao Disp. |
| Uniform |
20 |
20 |
0.01 |
0 |
0 |
0 |
|
|
|
|
|
|
|
3 |
1 |
2 |
| |
|
|
0.05 |
0 |
0 |
0 |
|
|
|
|
|
|
|
6 |
3 |
9 |
| |
|
|
0.1 |
0 |
0 |
0 |
|
|
|
|
|
|
|
10 |
8 |
13 |
| |
20 |
50 |
0.01 |
0 |
0 |
0 |
|
|
|
|
|
|
|
2 |
2 |
3 |
| |
|
|
0.05 |
0 |
0 |
0 |
|
|
|
|
|
|
|
4 |
5 |
9 |
| |
|
|
0.1 |
0 |
0 |
0 |
|
|
|
|
|
|
|
10 |
11 |
14 |
| |
20 |
100 |
0.01 |
0 |
0 |
0 |
|
|
|
|
|
|
|
2 |
0 |
5 |
| |
|
|
0.05 |
0 |
0 |
0 |
|
|
|
|
|
|
|
6 |
3 |
10 |
| |
|
|
0.1 |
0 |
0 |
0 |
|
|
|
|
|
|
|
11 |
8 |
14 |
| |
50 |
50 |
0.01 |
0 |
0 |
0 |
0 |
0 |
0 |
|
|
|
|
0 |
0 |
0 |
| |
|
|
0.05 |
0 |
0 |
0 |
0 |
0 |
0 |
|
|
|
|
4 |
7 |
3 |
| |
|
|
0.1 |
0 |
0 |
0 |
0 |
0 |
0 |
|
|
|
|
7 |
14 |
9 |
| |
50 |
100 |
0.01 |
0 |
0 |
0 |
0 |
0 |
0 |
|
|
|
|
1 |
2 |
2 |
| |
|
|
0.05 |
0 |
0 |
0 |
0 |
0 |
0 |
|
|
|
|
2 |
5 |
4 |
| |
|
|
0.1 |
0 |
0 |
0 |
0 |
0 |
0 |
|
|
|
|
7 |
8 |
9 |
| |
100 |
100 |
0.01 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
| |
|
|
0.05 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
4 |
8 |
4 |
| |
|
|
0.1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
10 |
13 |
10 |
| von Mises |
20 |
20 |
0.01 |
0 |
1 |
0 |
|
|
|
|
|
|
|
1 |
2 |
0 |
| |
|
|
0.05 |
2 |
4 |
1 |
|
|
|
|
|
|
|
8 |
5 |
7 |
| |
|
|
0.1 |
2 |
6 |
5 |
|
|
|
|
|
|
|
11 |
7 |
13 |
| |
20 |
50 |
0.01 |
0 |
1 |
0 |
|
|
|
|
|
|
|
1 |
2 |
0 |
| |
|
|
0.05 |
4 |
2 |
2 |
|
|
|
|
|
|
|
3 |
5 |
5 |
| |
|
|
0.1 |
9 |
5 |
5 |
|
|
|
|
|
|
|
9 |
6 |
11 |
| |
20 |
100 |
0.01 |
0 |
0 |
0 |
|
|
|
|
|
|
|
2 |
0 |
5 |
| |
|
|
0.05 |
1 |
2 |
3 |
|
|
|
|
|
|
|
7 |
2 |
8 |
| |
|
|
0.1 |
2 |
8 |
4 |
|
|
|
|
|
|
|
11 |
5 |
11 |
| |
50 |
50 |
0.01 |
0 |
0 |
0 |
0 |
0 |
1 |
|
|
|
|
0 |
0 |
5 |
| |
|
|
0.05 |
1 |
3 |
3 |
5 |
3 |
2 |
|
|
|
|
7 |
2 |
8 |
| |
|
|
0.1 |
6 |
3 |
9 |
8 |
5 |
9 |
|
|
|
|
13 |
5 |
9 |
| |
50 |
100 |
0.01 |
2 |
3 |
1 |
1 |
1 |
0 |
|
|
|
|
1 |
1 |
2 |
| |
|
|
0.05 |
6 |
8 |
9 |
3 |
5 |
4 |
|
|
|
|
9 |
4 |
7 |
| |
|
|
0.1 |
11 |
10 |
13 |
14 |
14 |
10 |
|
|
|
|
13 |
10 |
14 |
| |
100 |
100 |
0.01 |
1 |
0 |
0 |
1 |
0 |
0 |
0 |
1 |
4 |
4 |
2 |
0 |
1 |
| |
|
|
0.05 |
6 |
7 |
5 |
3 |
4 |
4 |
3 |
5 |
6 |
9 |
8 |
5 |
3 |
| |
|
|
0.1 |
11 |
13 |
14 |
11 |
11 |
7 |
10 |
8 |
10 |
10 |
12 |
11 |
11 |
| NNTS |
20 |
20 |
0.01 |
1 |
0 |
1 |
|
|
|
|
|
|
|
1 |
0 |
2 |
| |
|
|
0.05 |
6 |
3 |
2 |
|
|
|
|
|
|
|
6 |
6 |
8 |
| |
|
|
0.1 |
12 |
8 |
6 |
|
|
|
|
|
|
|
9 |
11 |
15 |
| |
20 |
50 |
0.01 |
1 |
0 |
0 |
|
|
|
|
|
|
|
2 |
0 |
7 |
| |
|
|
0.05 |
6 |
1 |
7 |
|
|
|
|
|
|
|
8 |
4 |
12 |
| |
|
|
0.1 |
7 |
5 |
11 |
|
|
|
|
|
|
|
10 |
8 |
16 |
| |
20 |
100 |
0.01 |
0 |
0 |
0 |
|
|
|
|
|
|
|
0 |
0 |
5 |
| |
|
|
0.05 |
3 |
1 |
1 |
|
|
|
|
|
|
|
2 |
2 |
9 |
| |
|
|
0.1 |
7 |
3 |
5 |
|
|
|
|
|
|
|
8 |
12 |
17 |
| |
50 |
50 |
0.01 |
4 |
0 |
0 |
1 |
1 |
1 |
|
|
|
|
3 |
0 |
3 |
| |
|
|
0.05 |
9 |
6 |
5 |
7 |
4 |
2 |
|
|
|
|
7 |
6 |
11 |
| |
|
|
0.1 |
17 |
14 |
14 |
12 |
10 |
5 |
|
|
|
|
14 |
14 |
15 |
| |
50 |
100 |
0.01 |
3 |
0 |
0 |
0 |
0 |
1 |
|
|
|
|
0 |
0 |
1 |
| |
|
|
0.05 |
5 |
6 |
8 |
7 |
6 |
2 |
|
|
|
|
6 |
7 |
10 |
| |
|
|
0.1 |
10 |
16 |
11 |
12 |
8 |
9 |
|
|
|
|
11 |
13 |
13 |
| |
100 |
100 |
0.01 |
1 |
0 |
1 |
0 |
2 |
1 |
3 |
3 |
1 |
3 |
0 |
0 |
0 |
| |
|
|
0.05 |
4 |
2 |
3 |
4 |
7 |
5 |
5 |
8 |
4 |
5 |
8 |
2 |
8 |
| |
|
|
0.1 |
11 |
6 |
7 |
9 |
14 |
12 |
15 |
14 |
12 |
11 |
12 |
8 |
15 |
|
