Magnitude of the sum of complex i.u.d. random variables in the unit circle - MathOverflow most recent 30 from http://mathoverflow.net2013-05-18T09:03:13Zhttp://mathoverflow.net/feeds/question/89478http://www.creativecommons.org/licenses/by-nc/2.5/rdfhttp://mathoverflow.net/questions/89478/magnitude-of-the-sum-of-complex-i-u-d-random-variables-in-the-unit-circleMagnitude of the sum of complex i.u.d. random variables in the unit circleRichard Bonne2012-02-25T11:23:36Z2012-02-25T13:43:22Z
<p>Hello everybody. I'm working about asymptotic estimates of</p>
<p>$M_n = \left|\sum_{k=1}^n Z_k\right|$</p>
<p>where $Z_1, Z_2, \ldots$ are independent uniformly distributed random variables on the complex unit circle. I found that the expectation $\textbf{E}[M_n^2] = n$ and the variance $\textbf{Var}[M_n^2] = n^2 - n$, so with Chebyshev's inequality I concluded that $M_n = o(n)$ almost surely as $n \to \infty$.</p>
<p>It is possible to improve this estimate? I need something like $M_n = O(\sqrt{n})$ or $M_n = O(n^{1/2 + \varepsilon})$. Thanks.</p>
http://mathoverflow.net/questions/89478/magnitude-of-the-sum-of-complex-i-u-d-random-variables-in-the-unit-circle/89479#89479Answer by Brendan McKay for Magnitude of the sum of complex i.u.d. random variables in the unit circleBrendan McKay2012-02-25T11:41:17Z2012-02-25T13:28:19Z<p>This is a classical random walk in the plane, extensively studied wrt Brownian motion etc.. Other people here are more expert in the subject than I am, so I'll leave it for them to provide you with references.</p>
<p>Also, because the summands are bounded, you can apply Hoeffding's inequality or similar to find strong tail bounds.</p>
http://mathoverflow.net/questions/89478/magnitude-of-the-sum-of-complex-i-u-d-random-variables-in-the-unit-circle/89486#89486Answer by Douglas Zare for Magnitude of the sum of complex i.u.d. random variables in the unit circleDouglas Zare2012-02-25T13:43:22Z2012-02-25T13:43:22Z<p>$O(\sqrt n)$ is too much to ask, and it fails almost surely. The <a href="http://en.wikipedia.org/wiki/Law_of_the_iterated_logarithm" rel="nofollow">law of the iterated logarithm</a> says the real and imaginary parts are a. s. $O(\sqrt{n \log \log n})$ so with probability $1$, $M_n$ is $O(\sqrt{n \log \log n})$.</p>