Let $\gamma_n$ denote the standard Gaussian measure on $\mathbb{R}^n$. It is known (see for example Cor 2.3 here: http://www.math.lsa.umich.edu/~barvinok/total710.pdf) that $$\gamma_n\{x\in\mathbb{R}^n: \|x\|^2 \ge \frac{n}{1-\epsilon}\}\ge e^{-\epsilon n/4}$$and $$\gamma_n\{x\in\mathbb{R}^n: \|x\|^2 \le (1-\epsilon)n\}\le e^{-\epsilon n/4}.$$

Given a Gaussian distribution with covariance $\Sigma$, what can we say about the distribution of the norm? That is, what happens if rather than the standard Gaussian we consider the measure induced by $N(\vec{0},\Sigma)$, for some positive semidefinite $\Sigma$?

I think we should get similar bounds along the lines of $$\gamma_n\{x\in\mathbb{R}^n: \|x\|^2 \ge \frac{\operatorname{Trace}(\Sigma)}{1-\epsilon}\}\ge e^{-\epsilon n/4}$$and $$\gamma_n\{x\in\mathbb{R}^n: \|x\|^2 \le (1-\epsilon)\cdot\operatorname{Trace}(\Sigma)\}\le e^{-\epsilon n/4}.$$

I think it should be the trace since Tr$(\frac1n\sum x_i x_i^T) = \frac1n\sum \|x_i\|^2\to\mathbb{E} \|x\|^2$. Is this true? If so, is the derivation straightforward given the standard normal tail bound? It doesn't seem trivial, though often these things are simple modifications of the $N(0,I)$ case.

I believe one can reduce the problem to proving $$\gamma_n\{x\in\mathbb{R}^n: x^t \Sigma^{-1} x \ge \frac{\operatorname{Trace}(\Sigma)}{1-\epsilon}\}\ge e^{-\epsilon n/4}$$and $$\gamma_n\{x\in\mathbb{R}^n: x^t \Sigma^{-1} x \le (1-\epsilon)\cdot\operatorname{Trace}(\Sigma)\}\le e^{-\epsilon n/4},$$ but as the transformed variable leads to a factor of $|\det\Sigma|$, it seems like I'm on the wrong track.


Note: The following is not an answer, but merely some thoughts which might or might not be helpful to you.

First note that you confused(?) your inequality signs. I think you want $$ \gamma_{n}\left(\left\{ x\in\mathbb{R}^{n}\,\mid\,\left\Vert x\right\Vert ^{2}\geq\frac{n}{1-\varepsilon}\right\} \right){\color{red}\leq}e^{-\varepsilon n/4} $$ and $$ \gamma_{n}\left(\left\{ x\in\mathbb{R}^{n}\,\mid\,\left\Vert x\right\Vert ^{2}\geq\frac{{\rm Trace}\left(\Sigma\right)}{1-\varepsilon}\right\} \right){\color{red}\leq}e^{-\varepsilon n/4}. $$ Also note that this inequality would get better with larger values of $n$. But in general, this is not true. To see this, use e.g. $$ \Sigma=\left(\begin{matrix}1\\ & 0\\ & & \ddots\\ & & & 0 \end{matrix}\right), $$ or if you want your $\Sigma$ to be positive semidefinite, use $\frac{1}{L\left(n-1\right)}$ instead of the zeros on the diagonal, where $L$ is large. Your estimate would then imply (since $\left\Vert x\right\Vert ^{2}\geq\left|x_{1}\right|^{2}$) that $$ \mathbb{P}\left(\left|x_{1}\right|^{2}\geq\frac{1+\frac{1}{L}}{1-\varepsilon}\right)\leq\mathbb{P}\left(\left\Vert x\right\Vert ^{2}\geq\frac{1+\frac{1}{L}}{1-\varepsilon}\right)\leq e^{-\varepsilon n/4}\xrightarrow[n\to\infty]{}0, $$ which is absurd.

Hence, the (exponent of the) right hand side of your estimate somehow needs to involve ${\rm trace}\left(\Sigma\right)$ instead of $n$ (I think).

What follows is an adaptation of the argument you linked, but I get eventually stuck when I try to optimize the/find a good value of $\lambda$.

First, since $\Sigma$ is symmetric positive semidefinite, there is an orthogonal matrix $O\in\mathbb{R}^{n\times n}$ with $\Sigma=O \cdot {\rm diag}\left(\lambda_{1},\dots,\lambda_{n}\right)\cdot O^{T}$, where $\lambda_{1},\dots,\lambda_{n}\geq0$ are the eigenvalues of $\Sigma$. We can now define the square root $\sqrt{\Sigma}:=O\cdot {\rm diag}\left(\sqrt{\lambda_{1}},\dots,\sqrt{\lambda_{n}}\right) \cdot O^T\in\mathbb{R}^{n\times n}$ which satisfies $\sqrt{\Sigma}^{T}=\sqrt{\Sigma}$ and $\sqrt{\Sigma}\sqrt{\Sigma}=\Sigma$. Now, by well-known properties of the normal distribution, we conclude that $X:=\sqrt{\Sigma}g\sim N\left(0,\Sigma\right)$, where $g\sim N\left(0,{\rm id}\right)$ is a standard normal distributed random variable.

We also know that the standard normal distribution is invariant under orthogonal transformations, i.e. $h:=O^{T}g\sim N\left(0,{\rm id}\right)$. Finally, $$ \left\Vert X\right\Vert ^{2}=\left\Vert O{\rm diag}\left(\sqrt{\lambda_{1}},\dots,\sqrt{\lambda_{n}}\right)O^{T}g\right\Vert ^{2}=\left\Vert {\rm diag}\left(\sqrt{\lambda_{1}},\dots,\sqrt{\lambda_{n}}\right)h\right\Vert ^{2}=\sum_{i=1}^{n}\lambda_{i}h_{i}^{2}, $$ so that $\left\Vert X\right\Vert ^{2}$ has (as you noted yourself) expectation $$ \mathbb{E}\left\Vert X\right\Vert ^{2}=\sum_{i=1}^{n}\lambda_{i}\mathbb{E}h_{i}^{2}=\sum_{i=1}^{n}\lambda_{i}={\rm trace}\left(\Sigma\right), $$ since $\mathbb{E}h_{i}^{2}={\rm Var}\left(h_{i}\right)=1$, since $h\sim N\left(0,{\rm id}\right)$.

By reordering, we can assume $\lambda_{1}\geq\dots\geq\lambda_{j}>0=\lambda_{j+1}=\dots=\lambda_{n}$, where $j\in\left\{ 0,\dots,n\right\} $.

Now observe that the Markov/Chebyscheff inequality yields, for arbitrary $\lambda>0$, \begin{eqnarray*} \mathbb{P}\left(\left\Vert X\right\Vert ^{2}\geq{\rm trace}\left(\Sigma\right)+\delta\right) & = & \mathbb{P}\left(e^{\lambda\left\Vert X\right\Vert ^{2}}\geq e^{\lambda\left({\rm trace}\left(\Sigma\right)+\delta\right)}\right)\\ & \leq & e^{-\lambda\left({\rm trace}\left(\Sigma\right)+\delta\right)}\cdot\mathbb{E}\left(e^{\lambda\left\Vert X\right\Vert ^{2}}\right), \end{eqnarray*} where \begin{eqnarray*} \mathbb{E}\left(e^{\lambda\left\Vert X\right\Vert ^{2}}\right) & = & \mathbb{E}\left(e^{\sum_{i=1}^{n}\lambda\lambda_{i}h_{i}^{2}}\right)\\ & = & \prod_{i=1}^{j}\mathbb{E}\left(e^{\lambda\lambda_{i}h_{i}^{2}}\right), \end{eqnarray*} by stochastic independence of $\left(h_{1},\dots,h_{n}\right)$. The main point of the introduction of the $e^{\dots}$ term is this final identity, where we can pull the product out of the expectation by independence.

Finally, \begin{eqnarray*} \mathbb{E}\left(e^{\gamma h_{i}^{2}}\right) & = & \frac{1}{\sqrt{2\pi}}\cdot\int_{\mathbb{R}}e^{\gamma x^{2}}\cdot e^{-x^{2}/2}\,{\rm d}x\\ & = & \frac{1}{\sqrt{2\pi}}\cdot\int_{\mathbb{R}}e^{-\left(\sqrt{\frac{1}{2}-\gamma}x\right)^{2}}\,{\rm d}x\\ & \overset{\omega=\sqrt{\frac{1}{2}-\gamma}x}{=} & \frac{1}{\sqrt{2\pi}\cdot\sqrt{\frac{1}{2}-\gamma}}\cdot\int_{\mathbb{R}}e^{-\omega^{2}}\,{\rm d}\omega\\ & = & \frac{1}{\sqrt{1-2\gamma}} \end{eqnarray*} for $\gamma<\frac{1}{2}$.

All in all, we arrive at $$ \mathbb{P}\left(\left\Vert X\right\Vert ^{2}\geq{\rm trace}\left(\Sigma\right)+\delta\right)\leq e^{-\lambda\left({\rm trace}\left(\Sigma\right)+\delta\right)}\cdot\prod_{i=1}^{j}\frac{1}{\sqrt{1-2\lambda\lambda_{i}}}. $$ The problem is now to optimize this w.r.t. $0<\lambda<\frac{1}{2\lambda_{1}}$. One way to simplify(?) this is to use $$ e^{-\lambda\left({\rm trace}\left(\Sigma\right)+\delta\right)}\cdot\prod_{i=1}^{j}\frac{1}{\sqrt{1-2\lambda\lambda_{i}}}=e^{-\left[\lambda\left({\rm trace}\left(\Sigma\right)+\delta\right)-\frac{1}{2}\sum_{i=1}^{j}\ln\left(1-2\lambda\lambda_{i}\right)\right]}, $$ where one only has to optimize the exponent. Still, I neither see an easy way to determine the optimal value of $\lambda$, nor a really convenient choice of $\lambda$.

One choice inspired by your linked lecture notes is to use $\lambda=\frac{\delta/2}{{\rm trace}\left(\Sigma\right)+\delta}$ (because in the standard gaussian case, we have $n={\rm trace}\left(\Sigma\right)$, which is exactly the choice used in the lecture notes). This would yield \begin{eqnarray*} \mathbb{P}\left(\left\Vert X\right\Vert ^{2}\geq{\rm trace}\left(\Sigma\right)+\delta\right) & \leq & e^{-\delta/2}\cdot\prod_{i=1}^{j}\sqrt{\frac{{\rm trace}\left(\Sigma\right)+\delta}{{\rm trace}\left(\Sigma\right)+\delta-\delta\lambda_{i}}}, \end{eqnarray*} which still does not really seem that great.

I will try to find a good choice of $\lambda$ here. If I come up with something, I will edit the post.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.