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Let $Y$ denote a Gaussian random variable characterized by a mean $\mu$ and a variance $\sigma^2$. Consider $N$ independent and identically distributed (i.i.d.) copies of $Y$, denoted as $Y_1, Y_2, \ldots, Y_N$. Now, let's examine the number of $N$ for which the probability satisfies the inequality:

\begin{align} \mathbb{P}\left[\left|-\frac{1}{N}\sum_{i=1}^{N}\log f(Y_i)-h(Y)\right|\geq\delta\right]\leq\epsilon, \end{align}

Here, $f(y)$ represents the probability density function (pdf) of the Gaussian distribution, and $h(Y)$ represents the differential entropy of $Y$. It is worth noting that the expected value of the expression $-\frac{1}{N}\sum_{i=1}^{N}\log f(Y_i)$ is equal to $h(Y)$:

\begin{align} \mathbb{E}\left[-\frac{1}{N}\sum_{i=1}^{N}\log f(Y_i)\right] = h(Y). \end{align}

To analyze the probability inequality, we can employ a suitable concentration inequality. One approach is to consider the subgaussianity of the term $\log f(Y_i) = \frac12\log(2\pi\sigma^2) - \frac{(Y_i-\mu)^2}{2\sigma^2}$. Is considering the subgaussianity of $\log f(Y_i)$ a valid approach? If so, could you provide guidance on how to compute the precise value of the subgaussianity parameter for $-\frac{1}{N}\sum_{i=1}^{N}\log f(Y_i)$?

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$\newcommand{\de}{\delta}\newcommand{\ep}{\epsilon}\newcommand{\si}{\sigma}\newcommand{\R}{\mathbb R}$We have \begin{equation*} L_i:=-\ln f(Y_i) = c+Z_i^2/2, \end{equation*} where $c:=\frac12\ln(2\pi\si^2)$ and $Z_i:=\frac{Y_i-\mu}\si$, so that the $Z_i$'s are independent standard normal random variables (r.v.'s).

Clearly, the r.v.'s $L_i$ are not sub-Gaussian, because for any real $s>0$ \begin{equation*} Ee^{L_i^2/s^2}=\int_\R dz\,\frac1{\sqrt{2\pi}}e^{(c+z^2/2)^2/s^2-z^2/2}=\infty, \end{equation*} since $e^{(c+z^2/2)^2/s^2-z^2/2}\to\infty$ as $z\to\infty$.

However, letting \begin{equation*} X_i:=2(L_i-EL_i)=Z_i^2-1, \end{equation*} for all real $h<1/2$ we have \begin{equation*} R(h):=Ee^{hX_i}=e^{-h}\int_\R dz\,\frac1{\sqrt{2\pi}}e^{hz^2-z^2/2}=\frac{e^{-h}}{\sqrt{1-2h}}. \tag{1}\label{1} \end{equation*} Note that for $h\in[0,1/2)$ \begin{equation*} R(-h)\le R(h), \tag{2}\label{2} \end{equation*} since for $g(h):=\ln\dfrac{R(-h)}{R(h)}$ we have $g(0)=0$ and $g'(h)=-\dfrac{8h^2}{1-4h^2}\le0$ for $h\in[0,1/2)$.

We want to find $n$ such that \begin{equation*} p_n:=P\Big(\Big|-\frac1n\sum_{i=1}^n\ln f(Y_i)-h(Y)\Big|\ge\de\Big)\le\ep. \tag{3}\label{3} \end{equation*} Note that \begin{equation*} p_n=P(|S_n|\ge2n\de), \end{equation*} where \begin{equation*} S_n:=\sum_{i=1}^n X_i. \end{equation*} So, we want to find $n$ such that \begin{equation*} P(|S_n|\ge2n\de)\le\ep, \end{equation*} where $\de\in(0,\infty)$ and $\ep\in(0,1)$. For all real $h\in[0,1/2)$, in view of \eqref{2} and \eqref{1}, \begin{equation*} \begin{aligned} P(|S_n|\ge2n\de)&=P(S_n\ge2n\de)+P(-S_n\ge2n\de) \\ &\le e^{-2n\de h}Ee^{hS_n}+e^{-2n\de h}Ee^{-hS_n} \\ &=e^{-2n\de h}R(h)^n+e^{-2n\de h}R(-h)^n \\ &\le2e^{-2n\de h}R(h)^n =2e^{nl(h)}, \end{aligned} \end{equation*} where $l(h):=-h\de-h-\frac12\ln(1-2h)$. Next, the minimum of $l(h)$ over $h\in[0,1/2)$ occurs at $h=h_\de:=\frac\de{2(1+\de)}$, and $e^{l(h_\de)}=q(\de):=e^{-\de/2}\sqrt{1+\de}<1$.

So, \eqref{3} will hold if $2q(\de)^n\le\ep$, that is, if \begin{equation} n\ge n_{\de,\ep}, \end{equation} where \begin{equation} n_{\de,\ep}=\frac{\ln(\ep/2)}{\ln q(\de)}, \end{equation} so that $n_{\de,\ep}$ is the root $n$ of the equation $2q(\de)^n=\ep$. One may also note that \begin{equation} n_{\de,\ep}\sim\frac{4\ln(2/\ep)}{\de^2} \end{equation} as $\de\downarrow0$.

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