i*********e
发帖数: 783 | 1 binay dependent variables.
The predictor includes: categorical variables and numeric variables.
I use logistic regression?
Can I also use probit regression?
Any difference? |
D*G
发帖数: 471 | 2 virtually no difference in application。 just a matter of choice.
but logistic reg has a simpler mathematics when it comes to OR
interpretation. |
c********d
发帖数: 253 | 3 That depends on how you treat your error term. Probit model's error term is
normally distributed. logistic model can also be written as the latent
variable form of probit model, but its error term is logistic distributed. |
i*********e
发帖数: 783 | 4 logistic model can also be written as the latent
variable form of probit model, but its error term is logistic distributed.
What does this mean? |
s*********e
发帖数: 1051 | 5 你确定？
【在 D*G 的大作中提到】
: virtually no difference in application。 just a matter of choice.
: but logistic reg has a simpler mathematics when it comes to OR
: interpretation.
|
g*******i
发帖数: 258 | 6 The difference is minor. That's why in real applications, logistic is
dominent, except some econometricians would choose probit, which I don't
understand.
For multinomial case, things become different. The probit setting is more
flexible, but its estimation is much more time consuming. This is the reason
why you still don't see many real applications are done with probit.
【在 i*********e 的大作中提到】
: binay dependent variables.
: The predictor includes: categorical variables and numeric variables.
: I use logistic regression?
: Can I also use probit regression?
: Any difference?
|
A*******s
发帖数: 3942 | 7 a latent variable following a conditional normal distribution is convenient
for joint modeling, for example, Heckman two stage.
reason
【在 g*******i 的大作中提到】
: The difference is minor. That's why in real applications, logistic is
: dominent, except some econometricians would choose probit, which I don't
: understand.
: For multinomial case, things become different. The probit setting is more
: flexible, but its estimation is much more time consuming. This is the reason
: why you still don't see many real applications are done with probit.
|
g*******i
发帖数: 258 | 8 are you saying probit with Bayesian estimation proposed by Chib?
The biggest problem with probit is it needs to deal with truncated normal,
which bothers researchers for decades. Even now truncated normal takes lots
of time. In real applications, people rarely use probit, even though it is
free from the independence of irrelevant alternatives restriction. Most
people, including some well developed business packages, use logistic and
ignore this IIA assumption in trade of efficiency.
convenient
【在 A*******s 的大作中提到】
: a latent variable following a conditional normal distribution is convenient
: for joint modeling, for example, Heckman two stage.
:
: reason
|
A*******s
发帖数: 3942 | 9 don't really know what is Chib's model you mentioned, neither its connection
to the Heckman model.
what i said is
http://en.wikipedia.org/wiki/Heckman_correction
also some of the tobit models
http://en.wikipedia.org/wiki/Tobit_model
theoretically we can develop something similar by changing all normal dist
assumptions to logistic dist assumptions. but why bother to do that...
lots
【在 g*******i 的大作中提到】
: are you saying probit with Bayesian estimation proposed by Chib?
: The biggest problem with probit is it needs to deal with truncated normal,
: which bothers researchers for decades. Even now truncated normal takes lots
: of time. In real applications, people rarely use probit, even though it is
: free from the independence of irrelevant alternatives restriction. Most
: people, including some well developed business packages, use logistic and
: ignore this IIA assumption in trade of efficiency.
:
: convenient
|
c********d
发帖数: 253 | 10 Probit model有conjugate prior, logistic model没有。 |
i*********e
发帖数: 783 | 11
What is conjugate pior
【在 c********d 的大作中提到】
: Probit model有conjugate prior, logistic model没有。
|
i*********e
发帖数: 783 | 12 What does "error term logistic distributed" mean?
is
【在 c********d 的大作中提到】
: That depends on how you treat your error term. Probit model's error term is
: normally distributed. logistic model can also be written as the latent
: variable form of probit model, but its error term is logistic distributed.
|
i*********e
发帖数: 783 | 13 I am lost. Conjugate prior is in Bayesian Estimation. Any relationship
between the two?
【在 c********d 的大作中提到】
: Probit model有conjugate prior, logistic model没有。
|
s******j
发帖数: 7 | 14 应该是link function 不一样吧。他们对应的是 logistic link 和 identity link.
【在 i*********e 的大作中提到】
: binay dependent variables.
: The predictor includes: categorical variables and numeric variables.
: I use logistic regression?
: Can I also use probit regression?
: Any difference?
|
