h******3
发帖数: 190 | 1 I am confused about the two tests.
Does the degree of freedom of both the tests follow the following?
degree of freedom = number of unknown parameters in H1- number of unknown
parameters in H0 |
d********t
发帖数: 837 | 2 It's assuming asymptotic normal distribution, why do you need degree of
freedom?
【在 h******3 的大作中提到】
: I am confused about the two tests.
: Does the degree of freedom of both the tests follow the following?
: degree of freedom = number of unknown parameters in H1- number of unknown
: parameters in H0
|
h******3
发帖数: 190 | 3 It can be more than 1 dimension. The statistic is chi-square.
【在 d********t 的大作中提到】
: It's assuming asymptotic normal distribution, why do you need degree of
: freedom?
|
d********t
发帖数: 837 | 4 It's still related to the multivariate normal, so the d.f. equals the
dimension of your multivariate normal.
【在 h******3 的大作中提到】
: It can be more than 1 dimension. The statistic is chi-square.
|
s*r
发帖数: 2757 | |
h******3
发帖数: 190 | 6
And the degree of freedom is?
You need to determine the dimension of multivariate normal.
I don't get your logic.
【在 d********t 的大作中提到】
: It's still related to the multivariate normal, so the d.f. equals the
: dimension of your multivariate normal.
|
d********t
发帖数: 837 | 7 Do you know how to do a multivariate wald test? It should be quite obvious
how many parameters you have in the wald test.
【在 h******3 的大作中提到】
:
: And the degree of freedom is?
: You need to determine the dimension of multivariate normal.
: I don't get your logic.
|
d******g
发帖数: 130 | 8 I think it should be the number of parameters in your model.
【在 h******3 的大作中提到】
: I am confused about the two tests.
: Does the degree of freedom of both the tests follow the following?
: degree of freedom = number of unknown parameters in H1- number of unknown
: parameters in H0
|
