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This nding is in agreement with earlier results for the Poisson process Remark 211 The phase method A very useful interpretation of the renewal process {N (t)} can be given when the interoccurrence times X1 , X2 , have an Erlang distribution Imagine that tokens arrive according to a Poisson process with rate and that the arrival of each rth token triggers the occurrence of an event Then the events occur according to a renewal process in which the interoccurrence times have an Erlang (r, ) distribution with scale parameter The explanation is that the sum of r independent, exponentially distributed random variables with the same scale parameter has an Erlang (r, ) distribution The phase method enables us to give a tractable expression of the probability distribution of N (t) when the interoccurrence times have an Erlang (r, ) distribution In this case P {N (t) n} is equal to the probability that nr or more arrivals occur in a Poisson arrival process with rate You are asked to work out the equivalence in Exercise 25 Asymptotic expansion A very useful asymptotic expansion for the renewal function M(t) can be given under a weak regularity condition on the interoccurrence times This condition

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will be formulated in Section 82 For the moment it is suf cient to assume that the interoccurrence times have a positive density on some interval Further it is 2 assumed that 2 = E(X1 ) is nite Then it will be shown in Theorem 823 that

lim M(t)

t 2 = 1 1 2 2 1

(216)

[33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [52] [53] [54] [55] [56] [57] [58] [59] [60] [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] [71] [72] [73]

The approximation M(t)

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is practically useful for already moderate values of t provided that the squared coef cient of variation of the interoccurrence times is not too large and not too close to zero 212 The Excess Variable In many practical probability problems an important quantity is the random variable t de ned as the time elapsed from epoch t until the next renewal after epoch t More precisely, t is de ned as t = SN (t)+1 t; see also Figure 211 in which a renewal epoch is denoted by Note that SN (t)+1 is the epoch of the rst renewal that occurs after time t The random variable t is called the excess or residual life at time t For the replacement problem of Example 211 the random variable t denotes the residual lifetime of the light bulb in use at time t Lemma 212 For any t 0, E( t ) = 1 [1 + M(t)] t (217)

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Proof Fix t 0 To prove (217), we apply Wald s equation from Appendix A To do so, note that N (t) n 1 if and only if X1 + + Xn > t Hence the event {N (t) + 1 = n} depends only on X1 , , Xn and is thus independent of Xn+1 , Xn+2 , Hence E which gives (217)

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vb.net convert image to pdf

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vb.net convert image to pdf

vb.net convert image to pdf

vb.net convert image to pdf

vb.net convert image to pdf

vb.net convert image to pdf

vb.net convert image to pdf

vb.net convert image to pdf

vb.net convert image to pdf

vb.net convert image to pdf

vb.net convert image to pdf

vb.net convert image to pdf