The Coupon Collector's Problem (CCP) is very useful in many applications. However, the "default" CCP is relatively simple: suppose you have an urn containing $n$ pairwise different balls. Now you want to draw a ball from the urn with replacements until you have seen each of the $n$ balls at least one. Now you can compute the average waiting time to get the number of draws overall needed by the formula \begin{align} \mathbb{E}[X] = \sum_{i=1}^n \mathbb{E}[X_i] = nH_n \end{align} where $H_n$ is defined as the harmonic series and $\mathbb{E}$ is the expected value. Also, the random variable $X$ is defined as the random number of draws you have to make in order to get all $n$ balls at least once. $X_i$ denotes the additional number of draws one has to make in order to get from $i-1$ different balls to $i$ different balls drawn. Additionally, each ball has an equal probability of $1/n$.
Now consider an advanced CCP question: how does the formula change in case you want to draw $p\geq 1$ pairwise different balls (instead of only one as in the default CCP) per draw, called packets? In other words: Given an urn containing $n$ balls, how many balls do I need to draw in order to get all $n$ balls when drawing always $p\geq 1$ (pairwise different) balls out of the urn? The set of balls is drawn with replacement. (Therefore all balls of one package are different, but different packages can contain same balls.)
An answer gives this paper on top of page 20, and also this german lecture gives an answer on slide 229, 14.7b). A third -- at the same time very intuitive to get -- answer is given on the german Wikipedia, subsection "Päckchen".
Now two questions arise.
- Why do the answers in the paper and the lecture differ? If you plug in some numbers, you get different results for numbers above 1000.
- How do I get from these solutions to the one given on Wikipedia? For me it seems like an approximation of the real value, since it is very fast to compute compared to the "scientific" answers, and the results is always "in the near of" the results of the other computations.
Since I am interested in understanding the formula on Wikipedia, can anyone help understanding the equation how the formula is derived or give some insight?