[proofplan] We build a finite packing inside $\Theta_s$ by taking scaled $s$-sparse binary vectors. The Gilbert-Varshamov packing gives exponentially many such vectors with pairwise Hamming distance of order $s$, hence squared Euclidean separation of order $a^2s$ after scaling by an amplitude $a>0$. We choose $a^2$ proportional to $\sigma^2\log(d/s)$ so that the Gaussian Kullback-Leibler divergences from the zero-mean model remain a small fraction of the logarithm of the packing cardinality. [Fano's inequality](/theorems/1654) then forces a constant probability of misidentifying the packing point, and the [testing-to-estimation reduction](/theorems/5895) converts that testing error into squared-error risk. [/proofplan] [step:Construct a large sparse Hamming packing] Let $\mathcal S_s \subset \{0,1\}^d$ denote the set of binary vectors with exactly $s$ nonzero coordinates: \begin{align*} \mathcal S_s := \left\{v \in \{0,1\}^d : \sum_{j=1}^d v_j = s\right\}. \end{align*} For $u,v \in \{0,1\}^d$, define the Hamming distance \begin{align*} d_H(u,v) := |\{j \in \{1,\dots,d\} : u_j \ne v_j\}|. \end{align*} By the Gilbert-Varshamov sparse packing bound (citing a result not yet in the wiki: Sparse Gilbert-Varshamov Bound), there are universal constants $c_{\mathrm{GV}}>0$ and a subset $\mathcal V \subset \mathcal S_s$ such that \begin{align*} |\mathcal V| \ge \exp\!\left(c_{\mathrm{GV}}s\log(d/s)\right) \end{align*} and, for all distinct $u,v \in \mathcal V$, \begin{align*} d_H(u,v) \ge \frac{s}{2}. \end{align*} Let $M := |\mathcal V|$. [/step] [step:Scale the packing into the parameter space and compute its separation] Let $a>0$ be an amplitude to be chosen later. For each $v \in \mathcal V$, define the parameter vector \begin{align*} \theta_v \in \mathbb R^d, \qquad (\theta_v)_j := a v_j \quad \text{for } j \in \{1,\dots,d\}. \end{align*} Since every $v \in \mathcal V$ has exactly $s$ nonzero coordinates, each $\theta_v$ belongs to $\Theta_s$. For distinct $u,v \in \mathcal V$, the squared Euclidean distance between $\theta_u$ and $\theta_v$ is \begin{align*} |\theta_u-\theta_v|^2 = \sum_{j=1}^d a^2(u_j-v_j)^2. \end{align*} Because $u_j,v_j \in \{0,1\}$ for each $j \in \{1,\dots,d\}$, the factor $(u_j-v_j)^2$ equals $1$ exactly on the coordinates where $u_j \ne v_j$ and equals $0$ otherwise. Hence \begin{align*} |\theta_u-\theta_v|^2 = a^2 d_H(u,v). \end{align*} Using the Hamming separation of the packing gives \begin{align*} |\theta_u-\theta_v|^2 \ge \frac{a^2s}{2}. \end{align*} Thus the finite family $\{\theta_v : v \in \mathcal V\}$ is separated in squared Euclidean distance by at least $a^2s/2$. [/step] [step:Choose the amplitude so the Gaussian information is small] Let $P_0 := \mathcal N(0,\sigma^2 I_d)$ and, for $v \in \mathcal V$, let $P_v := P_{\theta_v}=\mathcal N(\theta_v,\sigma^2 I_d)$. The Kullback-Leibler divergence from $P_v$ to $P_0$ is \begin{align*} D_{\mathrm{KL}}(P_v\,\|\,P_0) = \frac{|\theta_v|^2}{2\sigma^2}. \end{align*} Indeed, the two Gaussian measures have the same covariance matrix $\sigma^2 I_d$, and their Lebesgue densities differ only by the mean shift. Since $|\theta_v|^2=a^2s$, this gives \begin{align*} D_{\mathrm{KL}}(P_v\,\|\,P_0) = \frac{a^2s}{2\sigma^2}. \end{align*} Choose \begin{align*} a^2 := \frac{c_{\mathrm{GV}}}{16}\,\sigma^2\log(d/s). \end{align*} Then, for every $v \in \mathcal V$, \begin{align*} D_{\mathrm{KL}}(P_v\,\|\,P_0) = \frac{c_{\mathrm{GV}}}{32}s\log(d/s) \le \frac{1}{32}\log M, \end{align*} because $\log M \ge c_{\mathrm{GV}}s\log(d/s)$. [guided] The amplitude $a$ controls two competing quantities. Larger $a$ makes the parameters easier to separate in squared loss, because the separation is proportional to $a^2s$. But larger $a$ also makes the distributions easier to distinguish statistically, because the Kullback-Leibler divergence from the zero-mean Gaussian grows like $a^2s/\sigma^2$. For each $v \in \mathcal V$, define $P_v := \mathcal N(\theta_v,\sigma^2 I_d)$ and $P_0 := \mathcal N(0,\sigma^2 I_d)$. Since both Gaussian measures have covariance matrix $\sigma^2 I_d$, the Gaussian Kullback-Leibler formula gives \begin{align*} D_{\mathrm{KL}}(P_v\,\|\,P_0) = \frac{|\theta_v|^2}{2\sigma^2}. \end{align*} Here $|\theta_v|^2=a^2s$, because $\theta_v$ has exactly $s$ nonzero coordinates and each nonzero coordinate equals $a$. Hence \begin{align*} D_{\mathrm{KL}}(P_v\,\|\,P_0) = \frac{a^2s}{2\sigma^2}. \end{align*} We now choose $a$ so that this information is only a small fraction of the logarithm of the packing size. Since the Gilbert-Varshamov packing satisfies \begin{align*} \log M \ge c_{\mathrm{GV}}s\log(d/s), \end{align*} set \begin{align*} a^2 := \frac{c_{\mathrm{GV}}}{16}\,\sigma^2\log(d/s). \end{align*} Substitution gives \begin{align*} D_{\mathrm{KL}}(P_v\,\|\,P_0) = \frac{c_{\mathrm{GV}}}{32}s\log(d/s) \le \frac{1}{32}\log M. \end{align*} This is the exact information condition needed for Fano's inequality: the distributions indexed by the packing are far apart as parameters, but still too close statistically to identify reliably. [/guided] [/step] [step:Convert estimation risk into testing error on the packing] Let $\hat{\theta}: \mathbb R^d \to \mathbb R^d$ be an arbitrary measurable estimator. Define the induced decoder \begin{align*} \hat v: \mathbb R^d \to \mathcal V \end{align*} by choosing, for each $x \in \mathbb R^d$, an element $\hat v(x) \in \mathcal V$ satisfying \begin{align*} |\hat{\theta}(x)-\theta_{\hat v(x)}| = \min_{w \in \mathcal V} |\hat{\theta}(x)-\theta_w|. \end{align*} Ties are broken by a fixed ordering of the finite set $\mathcal V$. Let \begin{align*} \rho^2 := \min_{\substack{u,v \in \mathcal V\\u\ne v}}|\theta_u-\theta_v|^2. \end{align*} From the separation estimate, $\rho^2 \ge a^2s/2$. If $X \sim P_v$ and $\hat v(X)\ne v$, then the nearest-packing-point rule implies \begin{align*} |\hat{\theta}(X)-\theta_v| \ge \frac{\rho}{2}. \end{align*} Therefore \begin{align*} \mathbb E_v[|\hat{\theta}(X)-\theta_v|^2] \ge \frac{\rho^2}{4}\,\mathbb P_v(\hat v(X)\ne v) \ge \frac{a^2s}{8}\,\mathbb P_v(\hat v(X)\ne v). \end{align*} Taking the supremum over $v \in \mathcal V$ and then the infimum over estimators gives \begin{align*} \mathfrak M(\Theta_s,|\cdot|^2) \ge \frac{a^2s}{8} \inf_{\tilde v} \sup_{v \in \mathcal V} \mathbb P_v(\tilde v(X)\ne v), \end{align*} where the infimum is over all measurable decoders $\tilde v:\mathbb R^d \to \mathcal V$. [/step] [step:Apply Fano's inequality to the sparse Gaussian testing problem] By Fano's inequality in the form with a common reference measure (citing a result not yet in the wiki: Fano Inequality with Average Kullback-Leibler Radius), if \begin{align*} \frac{1}{M}\sum_{v \in \mathcal V}D_{\mathrm{KL}}(P_v\,\|\,P_0) \le \frac{1}{32}\log M, \end{align*} then there is a universal constant $c_F>0$ such that \begin{align*} \inf_{\tilde v} \sup_{v \in \mathcal V} \mathbb P_v(\tilde v(X)\ne v) \ge c_F. \end{align*} The information condition holds by the preceding step, because every term in the average is at most $\frac{1}{32}\log M$. Hence \begin{align*} \mathfrak M(\Theta_s,|\cdot|^2) \ge \frac{c_F a^2s}{8}. \end{align*} Substituting the chosen value of $a^2$ yields \begin{align*} \mathfrak M(\Theta_s,|\cdot|^2) \ge \frac{c_F c_{\mathrm{GV}}}{128}\,\sigma^2 s\log(d/s). \end{align*} Thus the desired lower bound holds with the universal constant \begin{align*} c := \frac{c_F c_{\mathrm{GV}}}{128}. \end{align*} [/step]