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For $h\in L_0^2(P)$, truncate $h$ to $h_m=\max (-m,\min (h,m))$ and subtract $P{h}_m$. Dominated convergence gives $h_m\to h$ in L2 space, and the Cauchy-Schwarz inequality gives $P{h}_m\to 0$. Thus bounded centered directions are dense in the mean-zero L2 space. The measure defining the L2 norm is essential: density-weighted L2 space can contain functions outside the unweighted Lebesgue space.

The decomposition $\dot{\ell }={\Pi }_{\mathcal{N}}\dot{\ell }+\tilde{\ell }$ is orthogonal. Taking the inner product with the efficient score gives $P(\dot{\ell }\tilde{\ell })=\tilde{I}$. Centering of the efficient score follows because the nuisance tangent space and the parametric score function lie in the closed mean-zero L2 space.

Fix $P=P_{\theta ,\eta }$. Let $\mathcal{N}$ be the nuisance tangent space: the closed linear span in $L^2(P)$ of score functions of statistical paths that vary only the nuisance parameter $\eta$. By the preceding argument, $\mathcal{N}$ is contained in the mean-zero L2 space $L_0^2(P)$.

Let ${\Pi }_{\mathcal{N}}$ denote orthogonal projection onto this closed subspace of a Hilbert space. The efficient score and scalar efficient information areThe efficient score is the component of the parametric score function that cannot be reproduced by changing the nuisance parameter. The efficient information is its squared L2 norm; it can be zero, so positivity must not be assumed in the definition.

The map $h\mapsto Ph$ is a bounded linear functional on L2 space, since $|Ph|\le \Vert h{\Vert }_2$ by the Cauchy-Schwarz inequality. Its kernel, the mean-zero L2 space, is therefore closed. Every nuisance score function and the parametric score function $\dot{\ell }$ are centered, so both ${\Pi }_{\mathcal{N}}\dot{\ell }$ and $\tilde{\ell }$ are centered. HenceMoreover, the efficient score belongs to the orthogonal complement of the nuisance tangent space. Writing $\dot{\ell }=\tilde{\ell }+{\Pi }_{\mathcal{N}}\dot{\ell }$ givesThis is the efficient-score projection identity; it remains valid when the efficient information is zero.

A statistical tangent set is the collection of score functions attained by a specified family of differentiable-in-quadratic-mean paths through $P$. The choice of paths is part of the definition. Its elements lie in the mean-zero L2 space, but the set need not already be a closed vector subspace.