Let $X$ be a sample space. and $p$ be a pdf(pmf). Let supp(p)=$\{x:p(x)>0\}$.Letting $X$ be redefined as supp(p), is equivalent to assuming that $p(x;\xi)>0$ holds for all $\xi\in E$ and for all $x\in X$.This means that $S$ is a subset of $$P(X)=\{p:X\to\mathbb{R}:p(x)>0(\forall x \in X),\int p(x)dx=1\}$$
The Coefficient of the Fisher information matrix is defined $$g_{ij}(\xi)=E_{\xi}[\partial_{i}l_{\xi}\partial_{j}l_{\xi}].............(1)$$ where, $E_{\xi}$ denotes the expectation with respect to the distribution $p_{\xi}.$ It is also possible to write $g_{ij}(\xi)$ as $$g_{ij}(\xi)=-E_{\xi}[\partial_{i}\partial_{j}l_{\xi}]...............(2)$$ and we also have $E_{\xi}[\partial_{i}l_{\xi}]=0.............(3)$
Now lets us define $S=\{p_{\xi}/\xi\in E\}$ as a subset of $$\bar{P}(X)=\{p:X\to\mathbb{R}/p(x)>0 \forall x\in X,\int p(x)dx<\infty\}$$
Then in this case the Fisher metric is the same as defined in equation $(1)$, But equation $(2)$ and $(3)$ does not hold. I didn't get why $(2)$ and $(3)$ does not hold for this case.Someone please explain. Thanks