# Upper bound on the l1 norm of a multivariate normal random variable

Let $X \sim {\cal N}_d(0, \sigma^2I_d)$. I am interested in bounding the tail probability $P[||X||_1 > t]$ from above. A pointer to a known exponential or polynomial tail bound would be appreciated.

One idea to deal with the above probability is to use something like McDiarmid's inequality, but suitable for unbounded random variables.

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A typical trick is to use Chebyshev's inequality with the function $\exp(\alpha x)$ to obtain $$P(\|X\|_1>t)\leq E(\exp(\alpha\|X\|_1))/\exp(\alpha t).$$ Since the 1-norm is a sum and the coordinates are independent, the right hand side equals $$\exp(-\alpha t)\ E(\exp(\alpha |Z|))^d=\exp(-\alpha t)\left(\exp(\alpha^2/2) (1+\mbox{erf}(\alpha/\sqrt{2})) \right)^d.$$ Here $Z$ is a one dimensional standard normal random variable.
You can now try to optimize over $\alpha$. Substituting the cheap upper bound $\mbox{erf}(\alpha/\sqrt{2})\leq 1$, and then optimizing gives $\alpha=t/d$. Plugging this in we get
$$P(\|X\|_1>t)\leq 2^d \exp(-t^2/2d).$$
(+1) Nice and clean. Note that the OP specified the variance as $\sigma^2$, but that's easy to incorporate. – cardinal Apr 20 '11 at 2:45
@cardinal Whoops, quite right I accidentally dropped the $\sigma$. I'll leave it as an exercise. – Byron Schmuland Apr 20 '11 at 2:52