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Suppose we have a random vector $\mathbf{X} = (X_1,\ldots,X_n)$ or sample with pdf or pmf $f(\mathbf{x};\theta)$, where $\theta \in \Theta$ is an unknown parameter (or vector of parameters), whose specification completely determines $f(\mathbf{x};\theta)$.

As far as I understand, a statistic is just a a random variable $T=T(\mathbf{x})$ that is a function (that does not depend on $\theta$) of $\mathbf{X}$. That's not to say the distribution of $T$ doesn't depend on $\theta$ of course, just that the function that relates $\mathbf{X}$ and $T$ does not.

Now, usually from what I've seen, the statistic $T$ is defined to be a sufficient statistic if the distribution of $\mathbf{X}$ conditional on $T=t$ does not depend on $\theta$.

Now, my issue with this definition (which I don't understand how is it not addressed anywhere I've looked) is that if the support of $T$ depends on $\theta$, then we can't really pick a $t$ in the support of $T$ and then claim that the conditional distribution does not depend on $\theta$, because that same conditional distribution may be ill-defined for some values of $\theta$ for which $t$ is not in the support of $T$, right?

I kinda came up with my own definition which I'm hoping someone could comment on to see if this is what's more formally meant by that usual definition:

A statistic $T(\mathbf{X})$ is sufficient for $\theta$ if there exists a non-negative function $C:\mathbb{R}^n\times \mathbb{R} \to \mathbb{R}$ that does not depend on $\theta$ and such that $$p(\mathbf{x},t;\theta) = C(\mathbf{x},t) \cdot q(t;\theta)$$ for all $\mathbf{x} \in \mathbb{R}^n, t\in \mathbb{R}, \theta \in \Theta$, where $p$ and $q$ denote the joint pdf/pmf of $(\mathbf{X},T(\mathbf{X}))$ and the pdf/pmf of $T(\mathbf{X})$, respectively.

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    $\begingroup$ (a) You're welcome to invent any kind of definition you want, but a definition by itself is without any utility. What matters is what you can use your definition for. Yours does not deserve to be called a "sufficient statistic" until you can show that all--or at least most--of the important properties and theorems enjoyed by sufficient statistics also hold for your definition. (b) I cannot make sense of the paragraph about "my issue." Could you elaborate on what you think the problem is? Perhaps offer a simple example? $\endgroup$ – whuber Oct 2 '16 at 22:07
  • $\begingroup$ Sure, for instance, consider $X_1,\ldots,X_n$ to be iid variables following a uniform distribution on the interval $[0,\theta]$. Now, if we consider the statistic $T(X_1,\ldots,X_n) = \sum_{i=1}^n X_i$, then it's clear that the support of $T$ is $[0,n\theta]$, which clearly depends on $\theta$. Now, if I pick any $t$, even if $t>0$, then how can I claim the conditional distribution of $\mathbf{X}=(X_1,\ldots,X_n)$ conditional on $T=t$ is a constant function of $\theta$, if that conditional distribution isn't even defined for, say $\theta = \frac{t}{2n}$? $\endgroup$ – user45453 Oct 2 '16 at 22:29
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    $\begingroup$ Maybe you should consult the Wikipedia article. $\endgroup$ – whuber Oct 2 '16 at 22:44
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    $\begingroup$ If you are interested, I found the answer to my question I suppose. The more rigorous definition (in the measure theory sense) does address the issue I mentioned by basically imposing that it's not the conditional distribution of $\mathbf{X}|T(\mathbf{X})=t$ itself that needs to be independent of $\theta$, but what they call a version of it. Anyways, here's a link on an article that rigorously defines it this way (page 232, definition 5). $\endgroup$ – user45453 Oct 2 '16 at 23:44
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    $\begingroup$ @user45453: You could write those details up and then answer your Q yourself $\endgroup$ – kjetil b halvorsen Jan 26 at 4:27
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The most frequently seen example of this is $X_1,\ldots,X_n\sim\operatorname{iid} \operatorname{Uniform}(0,\theta).$ It is said that the conditional distribution of $(X_1,\ldots,X_n)$ given $\max\{X_1,\ldots,X_n\}$ does not depend on $\theta.$

But to be precise, it ought to be said that it does not depend on which of the values of $\theta$ consistent with the observed value of $\max\{X_1,\ldots,X_n\}$ is the true value. Then one would take that to be the definition of sufficiency of the maximum.

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