An Orthogonal-Polynomial Approach to First-Hitting Times of Birth–Death Processes

Journal of Theoretical Probability, Dec 2015

In a recent paper in this journal, Gong, Mao and Zhang, using the theory of Dirichlet forms, extended Karlin and McGregor’s classical results on first-hitting times of a birth–death process on the nonnegative integers by establishing a representation for the Laplace transform \({\mathbb {E}}[e^{sT_{ij}}]\) of the first-hitting time \(T_{ij}\) for any pair of states i and j, as well as asymptotics for \({\mathbb {E}}[e^{sT_{ij}}]\) when either i or j tends to infinity. It will be shown here that these results may also be obtained by employing tools from the orthogonal-polynomial toolbox used by Karlin and McGregor, in particular associated polynomials and Markov’s theorem.

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An Orthogonal-Polynomial Approach to First-Hitting Times of Birth–Death Processes

An Orthogonal-Polynomial Approach to First-Hitting Times of Birth-Death Processes Erik A. van Doorn 0 0 Department of Applied Mathematics, University of Twente , P.O. Box 217, 7500, AE, Enschede , The Netherlands In a recent paper in this journal, Gong, Mao and Zhang, using the theory of Dirichlet forms, extended Karlin and McGregor's classical results on first-hitting times of a birth-death process on the nonnegative integers by establishing a representation for the Laplace transform E[esTi j ] of the first-hitting time Ti j for any pair of states i and j , as well as asymptotics for E[esTi j ] when either i or j tends to infinity. It will be shown here that these results may also be obtained by employing tools from the orthogonal-polynomial toolbox used by Karlin and McGregor, in particular associated polynomials and Markov's theorem. Birth-death process; First-hitting time; Orthogonal polynomials; Associated polynomials; Markov's theorem - 60J80 · 42C05 1 Introduction A birth–death process is a continuous-time Markov chain X := { X (t ), t ≥ 0} taking values in S := {0, 1, 2, . . .} with q-matrix Q := (qi j , i, j ∈ S) given by qi,i+1 = λi , qi+1,i = μi+1, qii = −(λi + μi ), qi j = 0, |i − j | > 1, where λi > 0 for i ≥ 0, μi > 0 for i ≥ 1 and μ0 ≥ 0. Positivity of μ0 entails that the process may evanesce by escaping from S, via state 0, to an absorbing state −1. Throughout this paper, we will assume that the transition probabilities Pi j (t ) := P(X (t ) = j | X (0) = i ), i, j ∈ S, satisfy both the backward and forward Kolmogorov equations, and mostly also that they are uniquely determined by the birth rates λi and death rates μi . Karlin and McGregor [14] have shown that the latter is equivalent to assuming ∞ n=0 1 πn + λnπn = ∞, where the πn are constants given by π0 := 1 and πn := λ0λ1 · · · λn−1 , n > 0. μ1μ2 · · · μn We note that condition ( 1 ) does not exclude the possibility of explosion, escape from S, via all states larger than the initial state, to an absorbing state ∞. We denote by Ti j the (possibly defective) first-hitting time of state j , starting in state i = j . Then, writing Pˆi j (s) := ∞ est Pi j (t )dt, s < 0, Fˆi j (s) = Pˆi j (s) Pˆ j j (s) , i = j Qi (s) Fˆi j (s) = Q j (s) , 0 ≤ i < j, ( 1 ) ( 2 ) ( 3 ) ( 4 ) and we have the well-known result 0 0 Fˆi j (s) := E[esTi j ] = ∞ est dP(Ti j ≤ t ), i = j, s < 0, (see, for example, [15, Equation (1.3)]). Karlin and McGregor give in [14, Equation (3.21)] a representation for Pˆi j (s), which upon substitution in ( 2 ) yields where Qn, n = 0, 1, . . . , are the birth–death polynomials associated with the process X , that is, the Qn satisfy the recurrence relation λn Qn+1(x ) = (λn + μn − x )Qn(x ) − μn Qn−1(x ), n > 0, λ0 Q1(x ) = λ0 + μ0 − x , Q0(x ) = 1. The representation ( 3 ) was observed explicitly for the first time by Karlin and McGregor themselves in [16, p 378]. Since then several authors have rediscovered the result or provided alternative proofs (see Diaconis and Miclo [4] for some references). In a recent paper in this journal, Gong et al. [12], using the theory of Dirichlet forms, extended Karlin and McGregor’s result by establishing a representation for the Laplace transform of the first-hitting time Ti j for any pair of states i = j , as well as asymptotics when either i or j tends to infinity. It will be shown here that these results may also be obtained by exploiting Karlin and McGregor’s toolbox, which is the theory of orthogonal polynomials. Our findings, which are actually somewhat more general than those of Gong et al. are presented in Sect. 3 and proven in Sect. 4. In the next section, we introduce some further notation, terminology and preliminary results. Since a path between two states in a birth–death process has to hit all intermediate states, we obviously have So for notational simplicity—and without loss of generality—we will restrict ourselves to an analysis of T0n and Tn0 for n > 0. ( 5 ) ( 6 ) 2 Preliminaries We will use the shorthand notation and, following Anderson [1, Chapter 8], n i=0 n i=0 Kn := πi , Ln := (λi πi )−1, 0 ≤ n ≤ ∞, C := ∞ n=0 (λnπn)−1 Kn, D := (λnπn)−1(K∞ − Kn). ∞ n=0 We have K∞ + L∞ = ∞ by our assumption ( 1 ), while, obviously, K∞ = ∞ ⇒ D = ∞, L∞ = ∞ ⇒ C = ∞. Also, C + D = K∞ L∞, so ( 1 ) is actually equivalent to C + D = ∞. Whether the quantities C and D are infinite or not determines the type of the boundary at infinity (see, for example, Anderson [1, Section 8.1]), but also, as we shall see, the asymptotic behavior of the polynomials Qn of ( 4 ). Since the birth–death polynomials Qn satisfy the three-terms recurrence relation ( 4 ), they are orthogonal with respect to a positive Borel measure on the nonnegative real axis, and have positive and simple zeros. The orthogonalizing measure for the polynomials Qn (normalized to be a probability measure) is not necessarily uniquely determined by the birth and death rates, but there exists, in any case, a unique natural measure ψ , characterized by the fact that the minimum of its support is maximal. We refer to Chihara’s book [3] for properties of orthogonal polynomials in general, and to Karlin and McGregor’s papers [14,15] for results on birth–death polynomials in particular (see also [10, Section 3.1] for a concise overview). For our purposes, the following properties of birth–death polynomials are furthermore relevant. With xn1 < xn2 < · · · < xnn denoting the n zeros of Qn(x ), there is the classical separation result 0 < xn+1,i < xni < xn+1,i+1, i = 1, 2, · · · , n, n ≥ 1, so that the limits exist. We further let ξi := nl→im∞ xni , i = 1, 2, . . . . σ := il→im∞ ξi (possibly infinity). The numbers ξi may be defined alternatively as ξ1 := inf supp(ψ ) and ξi+1 := inf{supp(ψ ) ∩ (ξi , ∞)}, i ≥ 1, where supp stands for support. So knowledge of the (natural) orthogonalizing measure for the polynomials Qn implies knowledge of the numbers ξi . It is clear from the definition of ξi that Moreover, we have, for all i ≥ 1, 0 ≤ ξi ≤ ξi+1 ≤ σ, i ≥ 1. ξi+1 = ξi ⇐⇒ ξi = σ, as is evident from the alternative definition of ξi . By suitably interpreting [6, Equations (2.6) and (2.11)], it follows that ∞ i=1 ξi−1 1 = nl→im∞ 1 + μ0 Ln j=0 n (λ j π j )−1 j i=0 πi (1 + μ0 Li−1), where the left-hand side should be interpreted as infinity if ξ1 = 0. In particular, ( 7 ) ( 8 ) (9) Also, by [6, Theorem 2], C < ∞ or D < ∞ ⇐⇒ ∞ i=2 ξi−1 < ∞ and Since the polynomials Q(nl) are birth–death polynomials, they are orthogonal with respect to a unique natural (probability) measure ψ (l) on the nonnegative real axis. A key ingredient in our analysis is Markov’s theorem, which relates the Stieltjes transform of the measure ψ (l) to the polynomials Q(nl) and Q(nl+1), namely We note that ψ (l) is not necessarily the only orthogonalizing measure for the polynomials Q(nl), a setting usually not covered in statements of Markov’s theorem in the literature (see, for example, [3, p 89]). However, an extension of the original theorem that serves our needs can be found in Berg [2] (see in particular [2, Section 3], where the measure μ(0) corresponds to our ψ (l)). Q(nl)(0) = 1 + μl πl (Ln+l−1 − Ll−1), n, l ≥ 0, where L−1 := 0. Note that Q(n0)(0) = Qn(0) = 1 for all n if μ0 = 0. Defining the quantities ξi(l) and σ (l) in analogy to ( 7 ) and ( 8 ), we have, by [3, Theorem III.4.2], ξi(l) ≤ ξi(l+1) ≤ ξi(+l)1, l ≥ 0, i ≥ 1, Moreover, [5, Theorem 1] tells us that σ (l) = σ, l ≥ 0. ll→im∞ ξi(l) = σ, i ≥ 1. ξi−1 < ∞. Given a sequence of birth–death polynomials {Qn}, we obtain the sequence {Q(nl)} of associated polynomials of order l ≥ 0 by replacing Qn by Q(nl), λn by λn+l and μn by μn+l in the recurrence relation ( 4 ). Evidently, the polynomials Q(nl) are birth–death polynomials again, so Q(nl)(x ) has simple, positive zeros xn(l1) < xn(l2) < · · · < xn(ln) and we can write Q(nl)(x ) = Q(nl)(0) , n, l ≥ 0, n i=1 We will also have use for a classical result in the theory of continued fractions relating the Stieltjes transforms of the measures ψ (l) and ψ (l+1), namely x − s = λl + μl − s − λl μl+1 ∞ ψ (l+1)(dx ) 0 x − s −1 , (15) Again we refer to Berg [2, Section 4] for statements of this result in the generality required in our setting. Our final preliminary results concern asymptotics for the polynomials Q(nl) as n → ∞, which may be obtained by suitably interpreting the results of [17] (which extend those of [6]). We state the results in three propositions and give more details about their derivations in Sect. 4. Recall that ξ0(l) = −∞ and Qn(0) = 1 if μ0 = 0. Proposition 1 Let K∞ = L∞ = ∞. Then C = D = ∞, σ = 0 and, for l ≥ 0, an entire function with simple, positive zeros ξi(l), i ≥ 1. Proposition 3 Let K∞ < ∞ and L∞ = ∞. Then C = ∞ and, (i) for l = 0 and μ0 > 0, or l ≥ 1, (ii) if D = ∞, for l ≥ 0, lim n→∞ Q(nl)(0) = ∞; (iii) if D < ∞ and μ0 = 0, an entire function with simple zeros ξ1 = 0 and ξi+1 > 0, i ≥ 1; (iv) if D < ∞, for l = 0 and μ0 > 0, or l ≥ 1, an entire function with simple, positive zeros ξi(l), i ≥ 1. 3 Results Representations for E[esT0n I{T0n<∞}] and E[esTn0 I{Tn0<∞}] in terms of the polynomials Q(nl) are collected in the first theorem. Theorem 1 We have, for μ0 ≥ 0 and n ≥ 1, 1 E[esT0n I{T0n<∞}] = Qn(s) , s < xn,1, and, if C + D = ∞, E[esTn0 I{Tn0<∞}] = λnλπ0n Nl→im∞ Note that for s < 0, we have E[esT0n I{T0n<∞}] = E[esT0n ], so the representation (16) reduces to Karlin and McGregor’s result ( 3 ). The explicit representation (17) is new, but may be obtained by a limiting procedure from Gong et al. [12, Corollary 3.6], where a finite state space is assumed. By choosing s = 0 in (16) and (17) and using (11), we obtain expressions for the probabilities P(T0n < ∞) and P(Tn0 < ∞) that are in accordance with [15, an unnumbered formula on page 387 and Theorem 10]. For convenience, we state the results as a corollary of Theorem 1, but remark that a proof of (19) on the basis of (17) would require additional motivation in the case ξ1( 1 ) = 0. Corollary 1 ([15]) We have, for μ0 ≥ 0 and n ≥ 1, (16) (17) (18) (19) E[esTn0 I {Tn0<∞}] = 1 − L n−1 L ∞ , s < ξ1( 1 ), (20) ∞ i=1 ∞ i=1 where the infinite products are entire functions with simple, positive zeros ξi(n+1) and Assuming a denumerable state space, but under the condition C = ∞ and D < ∞, Gong et al. give in [12, Theorem 5.5 (a)] a representation for E[esTn0 ], s < 0, which is encompassed by Corollary 2. Indeed, in this case we have L ∞ = ∞, and hence, by (19), P(Tn0 < ∞) = 1. Asymptotic results for E[esT0n I{T0n <∞}] and E[esTn0 I{Tn0<∞}] as n → ∞ are summarized in the second theorem. Theorem 2 We have, for μ0 ≥ 0 and s < 0, and nl→im∞ E[esTn0 I{Tn0<∞}] = 1 + μ0 L ∞ i=1 ξi − s ⎪⎩ 0 ⎧ 0 ⎪⎨ ∞ ξi( 1 ) ( 1 ) ⎪⎩ i=1 ξi − s if C < ∞, D = ∞ if C = ∞, if C < ∞, D = ∞ if C = ∞, D < ∞. (21) (22) The infinite products in (21) and (22) are reciprocals of entire functions with simple, positive zeros ξi and ξi( 1 ), i ≥ 1, respectively. By (18), we have so (21) implies 1 lim P(T0n < ∞) = 1 + μ0 L∞ , n→∞ nl→im∞ E[esT0n | T0n < ∞] = ∞ ξi i=1 ξi − s if C < ∞, D = ∞, which generalizes [12, Theorem 4.6] where μ0 = 0 is assumed. (At the end of [12, Section 4], the authors remark that the case μ0 > 0 may be treated in a way analogous to the case μ0 = 0, but no explicit result is given.) If C = ∞ and D < ∞, we must have L∞ = ∞, and hence, by (19), P(Tn0 < ∞) = 1. So (22) implies nl→im∞ E[esTn0 ] = ∞ ξ ( 1 ) i i=1 ξi( 1 ) − s if C = ∞, D < ∞, which is [12, Theorem 5.5 (b)]. 4 Proofs 4.1 Proofs of Propositions 1–3 The conclusions regarding C and D in the Propositions 1, 2 and 3 are given already in ( 6 ), while the statements (i) in Propositions 2 and 3 are implied by (11). The other statements follow from results in [17], where two cases—corresponding in the setting at hand to μ0 = 0 and μ0 > 0—are considered simultaneously by means of a duality relation involving polynomials Rn and Rn∗. The asymptotic results for Rn may be translated into asymptotics for Qn if μ0 = 0, while the results for R∗, suitably n interpreted, give asymptotics for Qn if μ0 > 0, and for Q(nl) with l ≥ 1. Concretely, the statements in Proposition 1, Proposition 2 (ii) and Proposition 3 (ii) regarding the case x < 0 follow from [17, Lemma 2.4 and Theorems 3.1 and 3.3], while the results for x > 0 are implied by [17, Theorems 2.2, 3.6 and 3.8]. Proposition 2 (iii) follows from [17, Theorem 3.1] for l = 0 and μ0 = 0, and from [17, Corollary 3.2] for l = 0 and μ0 > 0, and for l ≥ 1. Proposition 3 (iii) is implied by [17, Theorems 2.2, 3.3 and 3.4 (ii)], while Proposition 3 (iv) is a consequence of [17, Corollary 3.2]. Finally, the fact that σ = 0 in the setting of Proposition 1 is stated, for example, in [17, Theorem 2.2 (iv)]. 4.2 Proof of Theorem 1 As observed already, substitution in ( 2 ) of Karlin and McGregor’s formula for Pˆi j (s) given on [14, Equation (3.21)] leads to ( 3 ) and hence, by analytic continuation, to (16). To obtain (17) we note that [15, Equation (3.21)] also yields 1 1 Pˆ10(s) = − λ0 + Q1(s) Pˆ00(s) = λ0 (λ0 + μ0 − s) Pˆ00(s) − 1 , which upon substitution in ( 2 ) leads to Moreover, by Karlin and McGregor’s representation formula for the transition probabilities Pi j (t ) (see [14, Section III.6]) we have P00(t ) = 0∞ e−xt ψ (dx ), where ψ is a (probability) measure with respect to which the polynomials Qn are orthogonal. Since the condition C + D = ∞ is equivalent to ( 1 ), it ensures that the transition probabilities are uniquely determined by the birth and death rates, whence ψ must be the natural measure (see [14]). So we have Subsequently applying (15) with l = 0, it follows that 0 0 Pˆ00(s) = and, by analytic continuation, Since Tn0 = Tn,n−1 + · · · + T10, while Tn,n−1, . . . , T10 are independent random variables, Markov’s theorem (14) implies that we can write E[esTn0 I{Tn0<∞}] = E[esTn,n−1 I{Tn,n−1<∞}] · · · E[esT10 I{T10<∞}] μ1 · · · μn Q(Nn−+n1)(s) = λ1 · · · λn · · · lim N →∞ Q(Nn−)n+1(s) lim N →∞ Q(N1)(s) Q(N2−)1(s) λ0 lim = λnπn N →∞ . Recalling (12), we conclude that this expression holds for s < ξ1( 1 ). 4.3 Proof of Theorem 2 Letting n → ∞ in (16) and applying the results of Propositions 1, 2 and 3 readily yields the first statement of Theorem 2. To prove the second statement, we employ Corollary 2. First note that, for a > 0 s and s ≤ 0, we have 1 ≤ 1 − a ≤ e−s/a , so that, for l ≥ 0, 1 ≤ ∞ i=1 provided ξ1(l) > 0. Defining C (l) and D(l) in analogy to ( 5 ) it is easily seen that C < ∞ ⇐⇒ C (l) < ∞, D < ∞ ⇐⇒ D(l) < ∞. So, assuming C < ∞ or D < ∞, we have, by (10), Hence σ (l) = σ = ∞, so that, by (13), ξi(l) → ∞ as l → ∞. As a consequence ∞ 1 i=1 ξi(l) < ∞, l ≥ 1. ∞ lim l→∞ i=1 s 1 − ξ (l) i = 1, s ≤ 0, and the result follows since L∞ < ∞ if C < ∞, whereas L∞ = ∞ if C = ∞ and D < ∞. 5 Concluding Remarks First we note that the result (24)—or rather a generalization of (24)—may be derived directly from the Kolmogorov differential equations and Karlin and McGregor’s representation formula for the transition probabilities Pi j (t ). The argument is given on [8, p 508] (and essentially already on [15, p 385]) and yields Note that as a consequence of (17) and (26), we have, for all m ≥ 0, 0 ∞ Q(m1)(x ) ψ ( 1 )(dx ) = λm+1πm+1 n→∞ π1 lim x − s which implies a partial extension of Markov’s theorem (14) to the effect that, for m ≥ 0 and l ≥ 1 (and l = 0 if μ0 > 0), 0 ∞ Q(ml)(x ) ψ (l)(dx ) = λm+l πm+l n→∞ πl lim x − s . (27) Using (14), (15), and the recurrence relation for the polynomials Q(nl), it may be shown by induction that (27) is actually valid for all l ≥ 0, m ≥ 0 and Re(s) < ξ1(l). Substitution s = 0 in (27) and (11) leads in particular to 0 ∞ Qm (x ) x 1 ψ (dx ) = λm πm n→∞ lim Q(nm−+m1−)1(0) Qn(0) L∞ − Lm−1 , = 1 + μ0 L∞ which is consistent with [15, Equations (9.9) and (9.14)] (also when ξ1 = 0). If we do not impose the condition C + D = ∞, the birth and death rates do not necessarily determine a birth–death process uniquely. However, as observed in [12], several results remain valid if C + D < ∞, provided they are interpreted as properties of the minimal process, which is the process with an absorbing boundary at infinity (and which is always associated with the natural measure for the polynomials Qn, see [7]). Concretely, if C + D < ∞ the arguments leading to Theorem 1, and hence Theorem 1 itself and Corollary 1, remain valid. Moreover, the results in [17] imply that, for l ≥ 0, C + D < ∞ ⇒ nl→im∞ Q(nl)(0) = , an entire function with simple, positive zeros ξi(l), i ≥ 1. (Note that this complements Propositions 1—3.) Hence, also (20) remains valid. Finally, letting n → ∞ in (16) and (20), we readily conclude that the results in Theorem 2 for C < ∞, D = ∞ are actually valid for C + D < ∞ as well. In the setting C + D < ∞, Gong et al. [12] pay attention also to the maximal process, the process that is characterized by a reflecting barrier at infinity. In this case, the measure featuring in the representation for P00(t ), and hence in (23), is not the natural measure. Although, applying the results of [7], the relevant measure can be identified and expressed in terms of a natural measure corresponding to a dual birth–death process, application of Markov’s theorem does not seem feasible in this case. Our final remark is the following. Choosing l = 0, letting s ↑ ξ1 in (15), and using the recurrence relation ( 4 ), we readily get 0 In fact, using (15) again, it is not difficult to generalize this result to which upon substitution in (25) leads to 0 0 (Since, by [9, Theorem 3.1], the condition above is equivalent to ξ1-recurrence of the process, this result may also be obtained by applying [13, Lemma 3.3.3 (iii)] to the setting at hand, see [11, Lemma 3.2].) It now follows from (22) that C = ∞, D < ∞ ⇒ If μ0 = 0 then ξ1 = 0, so the result does not take us by surprise, but for μ0 > 0 we regain an interesting extension of Proposition 3 (iv)—recently obtained by Gao and Mao [11, Lemma 3.4]—since it has consequences for the existence of quasi-stationary distributions. 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Erik A. van Doorn. An Orthogonal-Polynomial Approach to First-Hitting Times of Birth–Death Processes, Journal of Theoretical Probability, 2017, 594-607, DOI: 10.1007/s10959-015-0659-z