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The total variation distance isIf $P\ll Q$, the Kullback-Leibler divergence isPinsker's inequality states

The squared-loss Le Cam two-point lemma says that for two experiments $P_0,P_1$ with scalar parameters ${\theta }_0,{\theta }_1$,Indeed, classify the data as $0$ when $\hat{\theta }$ is closer to ${\theta }_0$ and as $1$ otherwise. On a classification error, the estimation error is at least $|{\theta }_1-{\theta }_0|/2$. The sum of the two testing error probabilities is at least $1-\mathrm{TV}(P_0,P_1)$. Averaging the two risks and then bounding their maximum proves the result.

Let $r=\lfloor \beta \rfloor$. The Hölder class $\mathcal{H}(\beta ,L)$ on $[0,1]$ consists of functions with derivatives through order $r$ whose $r$th derivative is Hölder of order $\beta -r$ with constant $L$, with the standard integer-order convention.

Fix any $x_0\in [0,1]$. First compare the constant regression functions $m_0=0$ and $m_1=a$ with $a=c_0{n}^{-1/2}$. Both belong to every Hölder class under the seminorm convention, and the normal-product divergence isPinsker and Le Cam therefore give a lower bound $c/n$.

For the smoothness-dependent term, use the supplied smooth bump $\psi (u)=e^{-1/(1-u^2)}{1}_{\{|u|\le 1\}}$, translated one-sidedly near a boundary when necessary, and compareChoose its fixed normalization so that $m_1\in \mathcal{H}(\beta ,L)$ whenever $a\le c_{1}L{h}^{\beta }$. The Gaussian divergence satisfiesTakewith the bandwidth and amplitude truncated at constants when this expression leaves $[0,1]$. Then the divergence remains bounded andThe same construction can be placed at every $x_0$, with a one-sided bump at the endpoints. Pinsker and Le Cam, combined with the constant alternatives, provewhere $c>0$ depends only on $\beta$.

With densities $p,q$,By Cauchy-Schwarz inequality,Also $(\sqrt{p}-\sqrt{q}{)}^2\le |p-q|$, so $H^2\le 2\mathrm{TV}$.

The Hellinger affinity is $\rho (P,Q)=\int \sqrt{pq}d\mu =1-H^2/2$. Product densities and Fubini's theorem give $\rho (P^n,Q^n)=\rho (P,Q{)}^n$, hence

Le Cam two-point lemma states, for squared-error estimation at parameter points ${\theta }_0,{\theta }_1$, thatTake ${\theta }_0=0$, ${\theta }_1=\delta =1/(4n)$. The one-observation uniform densities overlap on length $1-\delta$, so $H^2(P_0,P_1)=2\delta$. ThereforeThe first distance inequality gives $\mathrm{TV}(P_0^n,P_1^n)\le 1/\sqrt{2}$. Le Cam's lemma now yieldsThis proves the claim with the displayed universal positive constant.