Hello,
I was wondering how to derive the asymptotic expansion of the following function:
where
is the probability function of the i-th event, say.
I don't even know where to start, or if asymptotic expansion can be done.
Hello,
I was wondering how to derive the asymptotic expansion of the following function:
where
is the probability function of the i-th event, say.
I don't even know where to start, or if asymptotic expansion can be done.
I believe I should be more explicit in what I am looking for.
I have the following discrete function:
where is the probability of the object in the indexed set . Consider this probability distribution to be a theoretical distribution.
Now, from some experiments, say that I have collected the empirical probability distribution in order to build the following function:
Obviously, these two functions are mathematical expectations on and , wherein the first function, , is the theoretical expectation, while the second function, , is the observed expectation. Therefore, I wanted to calculate the error involved in terms of the asymptotic expansion of the difference in observed and theoretical probabilities.
That is, given the error term, , I am looking for an asymptotic expansion on in order to provide an asymptotic argument on the discrepancy between the two expectations above.
Hence, I am looking to express the error in terms of some asymptotic series so that it can be the asymptotic expansion of function . In other words, I want to find that series so that:
I don't even know how to start looking for . Any insights would be encouraging for me to explore further.
Ok, here's what I came up with.
The forward difference that you consider:
is the first-order Newton series approximation of the xlogx function:
Then, from this, we approximated function E = S - H as:
Now, we may consider approximating the expansion using n-th order Newton series approximations. That is, define the n-th order forward difference as:
where C(n,i) is the combination "n choose i".
Then, we to obtain the n-th order derivative approximation of f, we have:
Or, we may write:
And, the general expression for our discrepancy function E = S - H becomes:
as , naturally.
So, for instance, for , and using the function , we have the second-order approximation of E equal to:
How would you comment on this approach? I am really interested on your insights and suggestions.
I don't know about this Newton approximation, but there is obviously something wrong with your formulas: I guess you have to sum the approximation terms for up to the order you want. For Taylor approximation, for instance, , and not just , of course.
Anyway, I would be very surprised if you need third order approximation or beyond.