VALUES FROM EXCHANGEABLE SEQUENCES 73
2. The exponential power distribution has the survival function ¯
Fpxq expp1 exprpλxqαsq, λ, α ¡ 0, x ¥ 0 . Let α¡ 1. For λ ¡ 0, we can introduce a new parameter u 1
λα1α and
write the survival function depending on u and α as ¯ Fpuqpxq exp 1 exp xα uα1 , u¡ 0, x ¥ 0 .
Then, pu, xq ÞÑ ¯Fpuqpxq is logconcave in px, uq and ¯Fpuq is stochastically increasing in u¡ 0.
4.6
Ageing properties of order statistics and
record values from exchangeable sequences
Let X tX1, X2, . . .u be an infinite sequence of real valued exchangeable
random variables such that
Xi ˜fpYi, Uq and X fpY, Uq
for a random sequence Y tY1, Y2, . . .u with iid components, a real valued
random variable U , independent of Y , and measurable functions f , ˜f . Further, let r P N and let Z be the r-th order statistic based on X1, . . . , Xn, n P N,
n¥ r or let Z be the r-th record value based on X. We assume P pZ 8q 0. Then, according to Section 3.3,
PpZ P Aq »
R
PpZpuqP AqdPUpuq for A P BpRqq ,
where Zpuq, u P R, is the corresponding r-th order statistic resp. the r-th record value based on the iid sequence
Xpuq t ˜fpY1, uq, ˜fpY2, uq, . . . u .
Now, ageing properties of Z are considered. Clearly, we can apply Theorem 4.39. Hence, if the distributions of Zpuq and U satisfy the conditions of the theorem, then Z has the IFR (IFRA, NBU) property. But, in addition, it is of interest to have conditions that directly depend on the distributions of X, Xipuq (iP N, u P R) and U.
Corollary 4.42 Let PpU ¡ 0q P pX1puq ¡ 0q 1 and ¯Fpuqpxq P pX1puq ¡ xq for u, x ¥ 0. Assume that X1puq is stochastically increasing in u¥ 0.
(a) Ifpu, xq ÞÑ ¯Fpuqpxq is logconcave in px, uq and U P IFR, then Z P IFR. (b) If
p ¯Fpuqpxqqα¤ ¯Fpαuqpαxq
for all αP p0, 1q, x, u ¥ 0 and if U P IFRA, then Z P IFRA. (c) If
¯
Fpuqpxq ¤ ¯Fpαuqpαxq ¯Fpp1αquqpp1 αqxq for all αP p0, 1q, x, u ¥ 0 and if U P NBU, then Z P NBU. Proof
We show that the corollary follows from Theorem 4.39.
Let Gpuq, u¥ 0, be the distribution function of Zpuq. The condition that X1puq is stochastically increasing in u¥ 0 and Proposition 5.23 (p. 101) yield that Zpuq is stochastically increasing in u¥ 0.
Let H be the distribution function of the r-th order statistic resp. the r-th record value based on an (n-dimensional) iid vector resp. an iid sequence of standard exponential distributed random variables. Corollary 3.8 implies that
¯
Gpuqpxq ¯Hp ln ¯Fpuqpxqq .
(a) Assume that pu, xq ÞÑ ¯Fpuqpxq is logconcave in px, uq. We show that pu, xq ÞÑ ¯Gpuqpxq is logconcave in px, uq. Therefore, choose u1, u2, x1, x2 ¥ 0
and αP p0, 1q. Since H P IFR, ln ¯H is decreasing and concave (cf. Proposition 2.16). Consequently, ln ¯Gpαu1 p1αqu2qpαx 1 p1 αqx2q ln ¯Hr ln ¯Fpαu1 p1αqu2qpαx 1 p1 αqx2qs ¥ ln ¯Hrα ln ¯Fpu1qpx 1q p1 αq ln ¯Fpu2qpx2qs ¥ α ln ¯Hr ln ¯Fpu1qpx 1qs p1 αq ln ¯Hr ln ¯Fpu2qpx2qs α ln ¯Gpu1qpx 1q p1 αq ln ¯Gpu2qpx2q .
Thus, Theorem 4.39 implies ZP IFR. (b) Now, choose αP p0, 1q, x, u ¥ 0 and let
p ¯Fpuqpxqqα¤ ¯Fpαuqpαxq . Notice that ¯H is decreasing and
4.6. AGEING PROPERTIES OF ORDER STATISTICS AND RECORD
VALUES FROM EXCHANGEABLE SEQUENCES 75
for y¥ 0 (since H P IFRA, cf. Proposition 2.17) Hence, r ¯Gpuqpxqsα r ¯Hp ln ¯Fpuqpxqqsα ¤ ¯Hpα ln ¯Fpuqpxqq ¯Hp lnr ¯Fpuqpxqsαq ¤ ¯Hp ln ¯Fpαuqpαxqq ¯Gpαuqpαxq . (c) Let αP p0, 1q, x, u ¥ 0 and ¯
Fpuqpxq ¤ ¯Fpαuqpαxq ¯Fpp1αquqpp1 αqxq . Since H P NBU, we have
¯
Hpy tq ¤ ¯Hpyq ¯Hptq for y, t ¥ 0 . Consequently, considering again that ¯H is decreasing,
¯ Gpuqpxq ¯Hp ln ¯Fpuqpxqq ¤ ¯Hp lnr ¯Fpαuqpαxq ¯Fpp1αquqpp1 αqxqsq ¯Hp ln ¯Fpαuqpαxq ln ¯Fpp1αquqpp1 αqxqq ¤ ¯Hp ln ¯Fpαuqpαxqq ¯Hp ln ¯Fpp1αquqpp1 αqxqq ¯Gpαuqpαxq ¯Gpp1αquqpp1 αqxq .
Remark 4.43 The conditions in the corollary imply that Xi, i P N, is
IFR resp. IFRA or NBU (according to Theorem 4.39) and furthermore that pX1, . . . , Xnq, n P N, is associated (according to Corollary 5.17, p. 95).
The following Theorem presents conditions ensuring that the first order statis- tic Z X1:n based on X1, . . . , Xn, nP N, has certain ageing properties.
Theorem 4.44
(a) IfpX1, . . . , Xnq P MIFR3, then X1:nP IF R.
(b) IfpX1, . . . , Xnq P MIFRA2, then X1:nP IF RA.
Proof
Let F1:nbe the distribution function of X1:nand Fn the distribution function
of Xpnq pX1, . . . , Xnq. Note that for x P R:
¯
F1:npxq ¯Fnpx, . . . , xq .
(a) Assume thatpX1, . . . , Xnq P MIFR3. For x, s, t¥ 0 with s ¤ t, we find
¯ F1:npx tq ¯F1:npsq F¯npx t, . . . , x tq ¯Fnps, . . . , sq XpnqP MIFR3 ¤ F¯npx s, . . . , x sq ¯Fnpt, . . . , tq F¯1:npx sq ¯F1:nptq . Hence, X1:nP IF R.
(b) Assume thatpX1, . . . , Xnq P MIFRA2. Then
¯
F1:nα pxq F¯nαpx, . . . , xq
XpnqP MIFRA2
¤ F¯npαx, . . . , αxq F¯1:npαxq for x¥ 0 and α P p0, 1q. Hence, X1:nP IF RA.
(c) Assume thatpX1, . . . , Xnq P MNBU2. For x, t¥ 0, we have
¯ F1:npx tq F¯npx t, . . . , x tq XpnqP MNBU2 ¤ F¯npx, . . . , xq ¯Fnpt, . . . , tq F¯1:npxq ¯F1:nptq . Consequently, X1:nP NBU.
Remark 4.45 Theorem 4.44 remains valid if the assumption of exchangeabil- ity of the underlying random variables is dropped.
Chapter 5
Dependence Properties
In the following, we study the dependence structure of the models introduced in Chapter 3.
5.1
Dependence structure of generalized order
statistics
The model of generalized order statistics includes many models of random vectors with ordered components. Let Xpiq (i P N) be the i-th generalized order statistic based on parameters γ1, . . . , γi¡ 0 and a distribution function
F and X : pXp1q, . . . , Xpnqq, n P N, then
Xp1q¤ ¤ Xpnq (almost everywhere). (5.1) (5.1) alone suggests that X carries a high amount of positive dependence. Indeed, we will see immediately that X satisfies all the positive dependence notions defined in Section 2.5 if F is continuous.
Theorem 5.1 The vector of generalized order statistics X pXp1q, . . . , Xpnqq, n P N based on a continuous distribution function is MTP2. Moreover, any
marginal distribution of at least two different generalized order statistics has the MTP2 property.
The previous theorem is taken from Cramer [2006]. It is shown there that the vector pXp1q, . . . , Xpnqq of generalized order statistic based on a continuous distribution function F has abn
j1PF density which is MTP2.
Remark 5.2 Since the MTP2class is a subclass of the classes CI, CIS, RCSI
etc. (cf. Theorem 2.42), the vector X pXp1q, . . . , Xpnqq of generalized or- der statistics based on a continuous distribution function belongs to all these classes.
Dependence of spacings of generalized order statistics: Now, we consider spacings
Spjq Xpjq Xpj1q, 1¤ j ¤ n
of generalized order statistics Xp1q, . . . , Xpnq (nP N, Xp0q : 0) based on a distribution function F and parameters γ1, . . . , γn¡ 0. It turns out that the
dependence properties of pSp1q, . . . , Spnqq are related to ageing properties of F . The following result is due to Burkschat [2006].
Theorem 5.3
(a) Let F be DFR (IFR). Then,pSp1q, . . . , Spnqq is CIS (CDS).
(b) Let F be an absolutely continuous distribution function with Lebesgue den- sity f , hazard rate λF and left resp. right support endpoints αpF q resp.
ωpF q. Let ωpF q 8 and let f be positive on pαpF q, 8q. Moreover, let one of the following conditions be satisfied:
(i) Let γj γj1 ¥ 1, (1 ¤ j ¤ n 1), γn ¥ 1 and let f be logconvex
on pαpF q, 8q.
(ii) Let γj ¥ γj 1, (1¤ j ¤ n 1), f be DFR and λF be logconvex on
pαpF q, 8q.
Then, pSp1q, . . . , Spnqq is MTP2.
Furthermore, the author establishes similar conditions for spacings to be MRR2 and S-MRR2, where S-MRR2 is a stronger dependence concept than
MRR2. (For a definition of S-MRR2, we refer to Burkschat [2006].)