Definitions for an exponential family to be curved or flat? I was wondering how a curved exponential family is defined? Also how is a flat exponential family  defined?


*

*Is "curved" or "flat" defined for a family of probability distributions, or for a
parametrization of a family of probability distributions? By the
latter, I mean if it is possible that, for two parametrizations of
the same family of probability distributions, a parametrization is "curved" while the other parametrization isn't?

*I searched in some books, but their definitions aren't the same,
and I am wondering if they are equivalent and why?


*

*From Casella and Berger's Statistical Inference, p115:  

*From Casella and Berger's Statistical Inference, again, p137~138: 

is this a definition of "curved"?

*From Bickle and Doksum's Mathematical Statistics Vol I, p56~57 


*From a note by Charles J. Geyer 

An exponential family is convex (also called flat) if its natural
  parameter space is a convex subset of the full natural parameter space
  (dom c, where c is the cumulant function). 
An exponential family is curved if it is a smooth submodel of a full
  exponential family that is not itself a flat exponential family,
  where smooth means the natural parameter space is specified as the
  image of a twice continuously differentiable function from Rp for
  some p into the full natural parameter space.

Thanks and regards!
 A: I actualy understand in this way, that  full family means its parameter space contains all the possible parameters while curved family may have a restricted parameter space... and examples given by hejseb seem to explain this quite nicely.
A: In my class we used Casella & Berger, and it wasn't very obvious to me what the definition meant since it's quite technical. If you look at example 3.4.4 (p. 113), you see that in the end what they get is 
$$
f(x|\boldsymbol{\theta})=h(x)c(\boldsymbol{\theta})\text{exp}[w_1(\boldsymbol{\theta})t_1(x)+w_2(\boldsymbol{\theta})t_2(x)]\\
f(x|\boldsymbol{\theta})=h(x)c(\boldsymbol{\theta})\text{exp}\left[\sum_{i=1}^2w_i(\boldsymbol{\theta})t_i(x)\right]\\
$$
so we sum two terms ($k=2$) and the vector $\boldsymbol{\theta}=(\mu, \sigma^2)$ is of dimension 2. Hence it is a full exponential family distribution. Now, consider the case where we instead have $x\sim N(\mu, \mu^2)$. Then what you end up with is 
$$
f(x|\boldsymbol{\theta})=h(x)c(\boldsymbol{\theta})\text{exp}\left[\sum_{i=1}^2w_i(\mu)t_i(x)\right]\\
$$
so clearly in this case $d=1<k=2$. Thus, it is a curved exponential. This can also be seen by the fact that the parameter space is not an open set, it's just a curve. Maybe you should take a look at exercise 3.33, perhaps it could be helpful to get your hands dirty a little.
Edit: Also, example 3.4.8 is basically what I just said too. 
This might also be helpful.
