A Probabilistic Generalization of the Stirling Numbers of the Second Kind
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A probabilistic generalization of the Stirling numbers of the second kind José A. Adell1,∗, Alberto Lekuona1 Departamento de Métodos Estadísticos, Facultad de Ciencias, Universidad de Zaragoza, 50009 Zaragoza (Spain) Abstract Associated to each random variable Y having a finite moment generating function, we introduce a different generalization of the Stirling numbers of the second kind. Some characterizations and specific examples of such gen- eralized numbers are provided. As far as their applications are concerned, attention is focused in extending in various ways the classical formula for sums of powers on arithmetic progressions. Illustrations involving rising fac- torials, Bell polynomials, polylogarithms, and a certain class of Appell poly- nomials, in connection with appropriate random variables Y in each case, are discussed in detail. Keywords: generalized Stirling numbers of the second kind, sum of powers formula, difference operators, Bell polynomials, polylogarithms, Appell polynomials. 2000 MSC: primary 05A19, 60E05; secondary 11M35, 33C45 1. Introduction arXiv:1803.04914v1 [math.NT] 13 Mar 2018 Let N be the set of positive integers and N = N 0 . Unless otherwise 0 ∪{ } specified, we assume throughout this paper that f : R R is an arbitrary function, m, n, N N , and x R. We also denote→ by I (x) = xn the ∈ 0 ∈ n monomial function. ∗Corresponding author. Email addresses: [email protected] (José A. Adell), [email protected] (Alberto Lekuona) 1The authors are partially supported by Research Projects DGA (E-64), MTM2015- 67006-P, and by FEDER funds. Preprint submitted to Elsevier May 7, 2019 The celebrated formula for sums of powers on arithmetic progressions states that N B (x + N + 1) B (x) I (x + k)= n+1 − n+1 , (1) n n +1 Xk=0 where Bn(x) is the nth Bernoulli polynomial. Since the time of James Bernoulli (1655–1705), different generalizations of such sums have been ob- tained (see, for instance, Kannappan and Zhang [1], Guo and Zeng [2], Adell and Lekuona [3], Kim and Kim [4], and the references therein). On the other hand, the usual mth forward difference of f is defined as m m ∆mf(x)= ( 1)m−kf(x + k). (2) k − Xk=0 From our point of view, an interesting fact is that the sums in (1) can also be computed in terms of forward differences of the monomial function In(x). Actually, we have (see, for instance, Rosen [5, p. 199] or Spivey [6]) N n∧N N +1 I (x + k)= ∆mI (x), (3) n m +1 n Xk=0 mX=0 where n N = min(n, N). Computationally, formulas (1) and (3) are equiv- alent in∧ the sense that the computation of a sum of N +1 terms is reduced to the computation of a polynomial in N of degree n +1. Finally, denote by ∆mI (x) S(n, m; x) := n , m n, (4) m! ≤ the Stirling polynomials of the second kind, so that S(n, m) := S(n, m;0), m n, (5) ≤ are the classical Stirling numbers of the second kind (see Abramowitz and Stegun [7] or Roman [8, p. 60] for other equivalent definitions). Obviously, formula (3) can be rewritten in terms of S(n, m; x). In this paper, we consider the following probabilistic generalization of the polynomials S(n, m; x). Suppose that Y is a random variable having a finite moment generating function, i.e., Eer|Y | < , (6) ∞ 2 for some r > 0, where E stands for mathematical expectation. Let (Yj)j≥1 be a sequence of independent copies of Y and denote by S = Y + + Y , k N (S = 0). (7) k 1 ··· k ∈ 0 We define the Stirling polynomials of the second kind associated to Y as 1 m m S (n, m; x)= ( 1)m−kE(x + S )n, m n, (8) Y m! k − k ≤ Xk=0 as well as the Stirling numbers of the second kind associated to Y as S (n, m)= S (n, m;0), m n. (9) Y Y ≤ Note that if Y =1, we have from (4), (5), and (8) S1(n, m; x)= S(n, m; x), S1(n, m)= S(n, m). Motivated by various specific problems, different generalizations of the Stirling numbers S(n, m) have been considered in the literature (see, for instance, Comtet [9], Hsu and Shine [10], Mező [11], Luo and Srivastava [12], Mihoubi and Maamra [13], Wuyungaowa [14], Cakić et al. [15], Mihoubi and Tiachachat [16], and El-Desouky et al. [17], among many others). As far as the applications of the polynomials SY (n, m; x) are concerned, attention is focused in showing the following extension of formulas (3) and (4) N n∧N N +1 EI (x + S )= m!S (n, m; x), (10) n k m +1 Y Xk=0 mX=0 and its consequences. The versatility of formula (10) comes from the fact that each choice of the random variable Y gives us a different (sometimes unexpected) summation formula. This is illustrated in Section 4, where we obtain summation formulas involving rising factorials, Bell polynomials, and polylogarithms by considering random variables Y having the exponential, the Poisson, and the geometric distributions, respectively. Analogous formu- las are obtained for a certain class of Appell polynomials by considering the normal, the logistic and the hyperbolic secant distributions, among others. In Section 3, we give different characterizations of the generalized polyno- mials SY (n, m; x), as well as some extensions of formula (10), whereas specific examples of the generalized Stirling numbers SY (n, m) are presented in Sec- tion 4. To clarify the proof of such results, we have included in Section 2 a reformulation of equality (3). 3 2. The classical formula for sums of powers Our approach to this classical formula is based on the following two simple properties. The first one (see, for instance, Flajolet and Vepstas [18], Mu [19] or Adell and Lekuona [3]) says that k k f(x + k)= ∆mf(x), k N , (11) m ∈ 0 mX=0 whereas the second one states that m ∆ pn(x)=0, m = n +1, n +2,..., (12) for any polynomial pn(x) of exact degree n, as follows easily from (2). Theorem 2.1. We have N n∧N N +1 n∧N I (x + k)= m!S(n, m; x)= c (k)I (x + k), (13) n m +1 n,N n Xk=0 mX=0 Xk=0 where n∧N N +1 m c (k)= ( 1)m−k, k =0, 1,...,n N. (14) n,N m +1 k − ∧ mX=k Moreover, n∧N cn,N (k)= N +1. (15) Xk=0 Proof. Choosing f(x)= In(x) in (11) and taking into account (4), we have N N k k I (x + k)= m!S(n, m; x) n m Xk=0 Xk=0 mX=0 (16) N N k n∧N N +1 = m!S(n, m; x) = m!S(n, m; x), m m +1 mX=0 kX=m mX=0 where in the last equality we have used (4), (12), and the elementary combi- natorial identity N k N +1 = , m N. (17) m m +1 ≤ kX=m 4 Again by (4), the last term in (16) equals to n∧N N +1 m m n∧N ( 1)m−kI (x + k)= c (k)I (x + k), m +1 k − n n,N n mX=0 Xk=0 Xk=0 where cn,N (k) is defined in (14). Finally, we have n∧N n∧N n∧N−k N +1 k + l c (k)= ( 1)l n,N k + l +1 k − Xk=0 Xk=0 Xl=0 n∧N N +1 k + l = ( 1)l k + l +1 k − Xs=0 kX+l=s n∧N N +1 s s = ( 1)s−k = N +1. s +1 k − Xs=0 Xk=0 This shows (15) and completes the proof. Remark 2.2. If N n, then c (k)=1, k = 0, 1,...,N, and therefore the ≤ n,N last equality in (13) is trivial. If N > n, then the finite sequence cn,N (k), k =0, 1,...,n is an alternating sequence, since it can be checked that N−(n+1) n +1+ i n k + i c (k)=1+( 1)n−k − , k =0, 1, . , n. n,N − k n k Xi=0 − Details are omitted. Theorem 2.3. Let pn(x) be a polynomial of exact degree n. Then, N n∧N N +1 n∧N p (x + k)= ∆mp (x)= c (k)p (x + k), (18) n m +1 n n,N n Xk=0 mX=0 Xk=0 where the coefficients cn,N (k) are defined in (14). Proof. The proof is the same as that of Theorem 2.1 replacing the monomial In(x) by the polynomial pn(x) and applying formulas (11) and (12). We emphasize that the last two expressions in (18) are computation- ally equivalent. The last one should be used when the forward differences m ∆ pn(x) are difficult to evaluate. In this regard, note that the coefficients cn,N (k) can be easily computed and stored once and for all in order to use them in any specific example (see Section 4 and particularly Theorem 4.9). 5 3. The polynomials SY (n,m; x) In what follows, we denote by (Uj)j≥1 a sequence of independent identi- cally distributed random varaibles having the uniform distribution on [0, 1], and assume that (Uj)j≥1 and (Yj)j≥1, as given in (7), are mutually indepen- dent. We consider the difference operator ∆1f(x)= f(x + y) f(x), y R, y − ∈ together with the iterates 1 1 m ∆ 1 m f(x)=(∆ ∆ )f(x), (y ,...,y ) R , m N.