0
$\begingroup$

For any non-negative integer $n$ and some finite $r$, we introduce the notation $n_k$ which indicates the number of $\{X_1, X_2, \cdots , X_n\}$ belonging to the $k$-th distribution type, for $k=1, 2, \cdots, r$. Here, it is assumed that $X_i$'s are independent, and that $E[X_i|S_n]$ converges to $E[X_i]$, for $i=1, 2, \cdots, n$. Then, the following condition holds true.

There are at most $r$ different elements in the set of all distributions of the sequence $X_1, X_2, \cdots,$. Moreover, we have that

\begin{equation} \lim_{n\rightarrow \infty} \min_{1\leq k\leq r} \frac{n_k}{ln \sigma[S_n]} = \infty, \end{equation} and for $i$ given, there exists an $n\geq i$ such that

\begin{equation} \sup_x \bar{f}_{S_{n/i}}(x) < \infty \end{equation} where $S_n = \sum_{i=1}^{n}X_i$ and $S_{n/i} = S_n - X_i$, for $i=1, 2, \cdots , n$, and $\bar{f}_{S_{n/i}}(x)$ is the probability density function of the normalized version of $S_{n/i}$, i.e., $\frac{S_{n/i} - E[S_{n/i}]}{\sigma[S_{n/i}]}$ with expectation and standard deviation $E[S_{n/i}]$ and $\sigma[S_{n/i}]$, respectively.

My question is: Why $r$ should be a finite number, what if r is infinite (the number of different distributions goes to infinity)? Can we provide another condition that satisfies the above condition?

$\endgroup$
4
  • $\begingroup$ When $r$ is infinite you can't even get started, because then potentially each $X_i$ has its own distribution. $\endgroup$
    – whuber
    Commented Mar 10, 2022 at 12:55
  • $\begingroup$ So, what happens if we say each $X_i$ has its own distribution. I mean, can we state something similar\equivalent to the above condition in case each $X_i$ has different distributions? $\endgroup$ Commented Mar 10, 2022 at 13:00
  • $\begingroup$ It's impossible to say, because you haven't asserted anything: you have only listed a bunch of assumptions. $\endgroup$
    – whuber
    Commented Mar 10, 2022 at 13:03
  • $\begingroup$ I mean suppose that we have a sequence of independent, but not essentially identical distribution of $X_1$, $X_2, \cdots$. Then if it is possible to state a condition like above to make sure that $E[X_i| S_n]$ converges to $E[X_i]$? $\endgroup$ Commented Mar 10, 2022 at 13:09

1 Answer 1

0
$\begingroup$

$r$ need not be finite. Assuming your result is correct, it is sufficient that the condition on the $n_k$ holds for all $r$.

Proof by taking subsequences. Let $i$ and $r<\infty$ be given. Fix a set of $r$ distribution types including the type of $X_i$, and take the subsequence consisting only of those types. Write $\tilde S_n$ for the subsequence partial sums. Your condition on $n_k$ still applies to the subsequence (the numerator is the same and the denominator becomes smaller).

Your result thus says $E[X_i|\tilde S_n]\to E[X_i]$. But the full sequence partial sums $S_n$ cannot contain any more information about $X_i$ than $\tilde S_n$ do, because they are a function of $\tilde S_n$ and variables independent of the subsequence.

It does seem necessary that each type appears infinitely often, and there's still a gap between that and your condition, potentially allowing for more refinement.

$\endgroup$
2
  • $\begingroup$ Thank you. What do you mean by "each type appears infinitely often", you mean all $X_i$ has its own distribution? $\endgroup$ Commented Mar 11, 2022 at 10:46
  • $\begingroup$ No, I mean there are infinitely many different types and for each type there are infinitely many $X_i$ of that type. Having every $X_i$ be a different type would contradict the assumption about $n_k$. $\endgroup$ Commented Mar 12, 2022 at 2:10

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.