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I have a set of sequential data around of around 12k points. Each is a 1 or -1. They are split something like 53%/47%. I want to test the hypothesis that this sequence comes from a random walk with a constant parameter. What sort of statistical test should I use?

(I'd also be grateful for any book recommendations in this general area)

Thanks very much

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It sounds to me that what you have are the steps of a process that you want to test whether it is a time-homogeneous simple random walk (possibly asymmetric). So, can the problem be reasonably codified as follows? You observe $\xi_1,\xi_2,\ldots$ each in $\{+1,-1\}$ with $\mathbb P(\xi_i = +1) = p(i)$. You want to test the hypothesis that $p(\cdot)$ is actually a constant function. Is this right? If so, do you know (i.e., can you assume) anything further about the properties of $p(\cdot)$, e.g., a single change in level or that it is smooth, etc? –  cardinal Jan 5 '12 at 12:28
    
Yes, spot on. At this stage I'm trying simply to arrive at a confidence value with which I can reject the hypothesis of p(.) being a constant process. I don't know a priori the value of the asymmetry, although I'm happy that it's roughly 53/47. –  Omnium Jan 5 '12 at 14:04
    
Note that the variance of the sample mean is maximized when $p(\cdot)$ is constant. In other words, suppose $Y_1, Y_2,\ldots$ are $\mathrm{Ber}(p_i)$ in $\{0,1\}$. Then $\mathbb E \overline{Y} = n^{-1} \sum_i p_i = \bar{p}$ and $\mathrm{Var}(\overline{Y}) = n^{-2} \sum_i p_i (1-p_i) \leq n^{-1} \bar{p}(1-\bar{p})$ by Jensen's inequality. The right-hand side is the variance of $\overline{Y}$ if $p(\cdot)$ is constant (always equal to $\bar{p}$). Hence, heuristically we'd expect the walk to be "too close" to the "trend" $\sum_{i=1}^n p_i$ on average if $p(\cdot)$ were not constant. –  cardinal Jan 5 '12 at 15:49
    
You've rather lost me there I'm afraid. –  Omnium Jan 5 '12 at 17:16
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1 Answer

You could try Runs Test. Let $n_1$ bet the number of +1 runs and $n_2$ bet the number of -1 runs. You could use the test as in the wiki page.

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Thanks for amazingly fast response! I don't think it's quite what I'm looking for. It seems to be looking more at non-independence of neighbouring points. I know that my data points are entirely independent. The cumulative graph wanders away from the 53% trend. I don't believe that the 1/-1 split early on (data is time ordered) is generated in the same mix as later. I've produced simulated graphs and they don't wander away from the trend like the data - I'm convinced that it isn't a constant valued random walk, but I can't think how to assess the degree of certainty of that conclusion. –  Omnium Jan 5 '12 at 11:19
    
Are you interested to test the data come from 50%/50%.What did you mean by constant parameter? –  vinux Jan 5 '12 at 11:24
    
A random walk consisting of a +1 move 53% of the time, and a -1 move 47% of the time. –  Omnium Jan 5 '12 at 11:32
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