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Below is a simple determinant. I need to show that it is not 0, so that the corresponding matrix is invertible. $$ D = \begin{vmatrix} 0! & 1! & 2! & \ldots & x!\\ 1! & 2! & 3! & \ldots & (x+1)! \\ 2! & 3! & 4! & \ldots & (x+2)! \\ \vdots & \vdots & \vdots & \ldots & \vdots \\ y! & (y+1)! & (y+2)! & \ldots & (x+y)! \end{vmatrix} $$ Remark: As pointed out in the comments, obviously we must have $y=x$ in order to have a square matrix.

Obviously, we can factor out $0!1!\ldots y!$ and get entries which are falling factorials, but I do not see how to continue.

The determinant of a similar 3X3 matrix was considered here and a stronger statement was proved on the remainder that the determinant has module 4 (after division by the obvious factors).

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    $\begingroup$ I guess $x = y$? Or is this some kind of generalized determinant of a non-square matrix? $\endgroup$ Commented Aug 5, 2022 at 9:48
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    $\begingroup$ If $x = y$ then the determinant is $\prod_{i=1}^{x-1} i!^2$, which can be easily conjectured by computing the first few and is referenced in OEIS A055209. I haven't dug through the references to see which of them prove it. I wouldn't be surprised if this is an example in one of Kratthenthaler's papers on "Advanced Determinant Calculus". $\endgroup$ Commented Aug 5, 2022 at 10:00
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    $\begingroup$ Assuming $x=y$, dividing the columns by $0!,\ldots,x!$ and the lines by the same, we get the matrix of binomial coefficients or Pascal matrix, $S_{x+1}$ in the notation of the Wikipedia article I just linked, which explains by factorization why the determinant is $1$. So your determinant is just $\prod_{i=0}^x i!^2$. $\endgroup$
    – Gro-Tsen
    Commented Aug 5, 2022 at 10:06

3 Answers 3

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This is the Hankel determinant associated to the sequence $m_n = \mathbb{E}(X^n) = n!$ of moments of an exponential distribution with mean $1$. Some general results can be used to show that the sequence of Hankel determinants associated to the moments of a random variable are always positive iff the induced measure on $\mathbb{R}$ has infinite support, and some more general results can be used to exactly calculate the Hankel determinants as in the comments by calculating an appropriate sequence of orthogonal polynomials. The relevant orthogonal polynomials for the exponential distribution are the Laguerre polynomials.

I gather this is very classical material but I don't know a reference; you can see a writeup in slightly unusual language here.

Edit: Ah, here's a reference: Section 2.7 of Krattenthaler's Advanced Determinant Calculus.

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Let's write a more general matrix $M_n(t)$ of size $n\times n$ having entries $(i+j-2+t)!$ for $1\leq i,j\leq n$. Then, we claim that $$\det(M_n(t))=\prod_{k=1}^n(k-1)!\,(k-1+t)! \tag1$$ which gives your desired determinant by setting $t=0$ and $n=x+1=y+1$.

The proof for (1) can be given by the so-called Dodgson's Condensation with runs recursively. I can supply this if needed.

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A direct proof of T. Amdeberhan's identity (1) is as follows: we have $(i+j+t)!=(i+t)! f_j(i)$, where $f_j(x)=(x+1)(x+2)\ldots (x+j)$. Thus $$ \det ((i+j+t)!)_{0\leqslant i,j\leqslant n-1}=\prod_{i=0}^{n-1} (i+t)!\times \det (f_j(i))_{0\leqslant i,j\leqslant n-1}, $$ and since $f_j$ is a monic polynomial of degree $j$ we get for arbitrary numbers $x_0,\ldots,x_{n-1}$ $$ \det (f_j(x_i))_{0\leqslant i,j\leqslant n-1}= \det (x_i^{j-1})_{0\leqslant i,j\leqslant n-1}=\prod_{i<j} (x_j-x_i), $$ where the first equality follows from consecutive subtraction from the $j$-th column ($j=0,1,\ldots,n-1$) an appropriate linear combination of the previous columns; and the second equality is Vandermonde determinant.

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