# Is $F(E[Y_n]) \approx E[F(Y_n)]$ a reasonable approximation?

Studying the asymptotic distribution of order statistics I came across this approximation:

$$F \left( E \left[ Y_n^{\left(n \right)} \right] \right) \approx E \left[ F \left( (Y_n^{\left( n \right)} \right) \right]$$

where $Y_n^{\left( n \right) }$ denotes the sequence of the nth order statistics and $F(.)$ is the distribution function of the underlying distribution.

I understand that because of the Probability Integral Transform, $F \left( Y_n^{ \left(n \right)} \right)$ is the largest observation from a sample of size $n$ from a uniform distribution over $(0,1)$.

Intuitively it makes sense to claim that the area to the left of the mean of $Y_n^{ \left( n \right) }$ is approximately equal to the mean of the area to the left of $Y_n^{ \left( n \right) }$. Is that really the case though? Also does this approximation hold for other order stastistics as well?

All help is greatly appreciated, thank you.

• It's a good question, but in the interest of pursuing what might be an important issue, I would like to point out that the much simpler approximation $F(\mathbb{E}(Y_n^{(n)}))\approx 1/2$ already works pretty well: it is never off by more than $1/2$! The point, of course, is that it would behoove us to quantify the accuracy of your approximation and to establish a threshold to distinguish good from poor accuracy for your application. Do you have any thoughts about that? (BTW, the right hand side has a Beta distribution for any order statistic provided $F$ is a continuous distribution.)
– whuber
Jun 27 '14 at 17:15
• @whuber Indeed, it can be routinely derived that for the nth order statistic the RHS equals $\frac{n}{n+1}$. The authors use this approximation and the fact that the RHS can be computed to get an approximation for $E \left[ Y_n \right]$ which we need as our centering constant for the asymptotic distribution of $Y_n$ as sometimes it can hard to compute explicitly. Jun 27 '14 at 17:24
• The asymptotic distribution of $Y_n^{(n)}$ will be an extreme value distribution. I don't think it's important (theoretically) to have an accurate estimate of its center, but I suppose in practice with finite $n$ such an estimate might help, depending on the application.
– whuber
Jun 27 '14 at 17:40

It's the first term in a Taylor expansion - when taking only one term, it's a zero-order approximation.

It's quite common with Taylor expansions for moments of functions of random variables to take a second-order approximation (which would be 3 terms of the expansion, but when taking expectations the second term is zero).

The suggested Taylor approximation will only be accurate in some particular circumstances. I don't think that it will typically be the case for largest order statistics, but it might happen at least sometimes. The next non-zero term in the Taylor expansion should give some sense of the size of the bias.

• Very useful technique, thank you for pointing this out. Jun 27 '14 at 17:32
• By the way many of the examples I have been studying that use this approximation have a standard logistic distribution as the underlying pdf. Thus the approximation does not seem too bad, considering that $\sigma^2=\pi^2/3$ Jun 27 '14 at 17:45
• +1 It might be worth pointing out that the error of the $n$th Taylor expansion can be understood in terms of the $(n+1)$th derivative. See here. This gives a case-by-case basis for determining how useful the approximation is in a worst case scenario.
– user44764
Jun 27 '14 at 18:04
• @Matthew +1 However, in this particular case (where we take expectations of an expansion about the mean), the (0+1)th derivative is 0 (so the zero order approximation is accurate to first order), -- and our investigation of the error passes to the next term. Jun 27 '14 at 23:55

Jensen's Inequality states that for a random variable $Y$ and a convex function $\varphi$, we have $$\varphi(E[Y]) \leq E[\varphi(Y)].$$ If $\varphi$ is concave, then the direction of the inequality is switched. Furthermore, equality holds for non-constant $Y$ if and only if $\varphi$ is linear.

For large $n$, we expect $Y_n^{(n)}$ to be in the right tail of the distribution, where the distribution function $F$ is approximately linear for many common distributions. I believe this is where the given approximation would come from.

• Indeed, thank you. It would seem that the approximation does quite well in the right tail of $F$; it might not be unreasonable to expect it to hold for $Y_1$ as well as many cdf's are also linear in the left tail. Jun 27 '14 at 17:40