I am trying to solve a question in the book on Probability and Computing by Michael Mitzenmacher, Eli Upfal. The question asks to generalize ball-bin problem for 2-universal hashing to $k$-universal hashing.
In the standard bin-ball problem setup there are $n$ bins and $n$ balls. These $n$ balls are randomly thrown (independently) in $n$ boxes and we try to find maximum occupancy in any bin. This can be done by considering a hash function $h: [n] \to [n]$. We say that ball $i$ goes in bin $h(i)$. In this case hash function comes from a 2-universal family. It is shown that for 2-universal hash family, max occupancy is $l_{\max}=1+2\sqrt n$ with probability greater than $\frac{1}{2}$. It follows with usual application of Markov's inequality. Now we try to generalize this to hash $h$ coming from a $k$-universal family and find $l_{\max}$ so that max occupancy is less than that with probability $\frac{1}{2}$.
I tried to do following: First set up indicator variable as in previous case for 2-universal problem
$X_{ij}=1$ if ball i goes in bin $j$. Now load for bin $i$ is $X=\sum_{j=1}^{n}X_{ij}$.
Since hash is now $k$-universal we try to go for Markov on $k^{\text{th}}$ moment instead of Markov (which comes from 1-st moment) to get probability bound of $\frac{1}{2n}$. In the end we apply union bound. But I am unable to simplify expression for $k$-th moment in this context to get an answer.
Definition of $k$-universal hash function
$\mathrm{Pr}(h({i_{1}})=h({i_{2}})=\cdots=h({i_{k}}))= \frac{1}{n^{k-1}}$ for any $k$ distinct elements $i_1,\ldots,i_k$.