Higher central moments are very useful in statistical analysis: the third moment M3 characterizes asymmetry of the corresponding probability distribution, the fourth moment M4 describes the size of the distribution’s tails, etc. When we know the exact values x1, . . . , xn, we can use the known formulas for computing the corresponding sample central moments. In many practical situations, howeve...

The central moments of the multivariate normal distribution are functions of its n×n variance-covariance matrix Σ. These moments can be expressed symbolically as linear combinations of products of powers of the elements of Σ. A formula for these moments derived by differentiating the characteristic function is developed. The formula requires searching integer matrices for matrices whose n succe...

Let q be an odd prime power, and Hq,d denote the set of square-free monic polynomials D(x)∈Fq[x] of degree d. Katz and Sarnak showed that the moments, over Hq,d, of the zeta functions associated to the curves y(2)=D(x), evaluated at the central point, tend, as q→∞, to the moments of characteristic polynomials, evaluated at the central point, of matrices in USp(2⌊(d-1)/2⌋). Using techniques that...

We present the first general formulas for central and non-central moments of multinomial distribution, using a combinatorial argument factorial previously obtained in Mosimann (1962). use to give explicit expressions all up order 8 4. These results expand significantly on those Newcomer (2008) et al. (2008), where were calculated