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Background

Let $X_t$ be a stochastic process on the state space {Working, Broken}. Let $U$ be the cumulative sojourn Working during an interval $[0,\tau]$ (the process's uptime). It is well-known [1] that if the process has a finite mean time to failure (MTTF) and mean time to repair (MTTR), then $$ \begin{align*} P\left\{ X_t = \text{Working}\ | \ X_s = \text{Working} \right\} &= p + (1-p) \cdot z(t;s) \\ P\left\{ X_t = \text{Broken}\ | \ X_s = \text{Broken} \right\} &= 1-p + p \cdot z(t;s) \end{align*} $$ for some function $z$, where $p = \frac{\text{MTTF}}{\text{MTTF}+\text{MTTR}}$.

Now further suppose that the times to failure have finite variance, and so too for the times to repair. Then in a classic result [2], Takács showed $U$ is asymptotically normal as $ \tau \rightarrow \infty $, and calculated the distribution's mean $\mu_U$ and variance $\sigma^2_U$.

My situation

I can prove that $U$ is asymptotically normal and calculate $\mu_U$ and $\sigma^2_U$, but with two caveats:

  1. I need to make an assumption about $z$. Thus my result is less general than Takács's.

  2. Calculating $\sigma^2_U$ requires calculating $\lim_{\tau\rightarrow\infty} \int_{0}^{\tau} z(t; 0) \ dt$. Thus my result is less useful than Takács's, as solving $z$ for arbitrary processes is an open question.

However, the result is (perhaps?) of interest as an elementary application of Billingsley's central limit theorem for dependent variables under strong mixing [3, Theorem 27.4]. (And yes: What happened is that I learned about Billingsley's result through Wikipedia when first reviewing literature, found a proof, and then I found Takács result. There must be Murphy's Law variant for this.)

If Prof Billingsley was still alive, I'd probably write to him and offer the result as a homework exercise for a new edition of his book. However, failing that option ...

My question

... what journal might be good for my result?

(I know I can self-publish to arXiv or equivalent with my employer. I am interested in alternatives.)

Many thanks.

References

[1] Kishor Shridharbhai Trivedi, 2002, Probability and statistics with reliability, queuing, and computer science applications, Second ed., John Wiley & Sons, New York.

[2] Lajos Takács, 1959, On a sojourn time problem in the theory of stochastic processes, Transactions of the American Mathematical Society, 93(3), 531--540.

[3] Patrick Billingsley, 2008, Probability and measure, John Wiley & Sons, New York.

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Real Analysis Exchange has an Inroads section that specifically covers the case of "a clever new proof of an important theorem".

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