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Put $U=X/\sigma$. The standardized efficient score function for scale isAn optimal B-robust scale M-estimator solveswhere the optimal bounded score has the clipped-score formThe constants $A,z$ enforce Fisher consistency, the chosen normalization, and the clipping bound $b$ on the influence function.

For the standard normal distribution, $f^{\prime }(u)/f(u)=-u$, hence $\Lambda (u)=u^2-1$. The score therefore simplifies towith $z$ chosen so that $\mathbb{E}\psi (Z)=0$ for $Z\sim N(0,1)$.

Put $K=nY$. Apart from a factor independent of $p$, the likelihood isThus the score function and Fisher information matrix are

The Newton method and Fisher scoring updates are respectivelySince $\hat{p}=Y$, initialization at $p^{(0)}=Y$ requires zero steps for either algorithm. Moreover,so binomial-proportion Fisher scoring reaches the MLE in one step from every interior $p^{(0)}\ne Y$.

A statistical functional is pathwise differentiable relative to chosen statistical paths if its derivative along every path depends only on that path's score function and defines a bounded linear functional on their statistical tangent space. The Riesz representation theorem expresses this derivative as an L2 inner product with a unique element of the statistical tangent space, the canonical gradient. A path family and the associated derivative remainder conditions must both be specified; a formal derivative along one convenient family does not establish differentiability along all paths.

At a distinct event time, the Schoenfeld function is the event subject's covariate minus the hazard-weighted covariate mean over the risk set, as a function of a trial coefficient $\beta$. Its evaluation at the fitted coefficient is a Schoenfeld residual. The sum of these functions is the Cox partial likelihood score function, and each is conditionally centered at zero at the true proportional-hazards coefficient.

A score equation sets the score function equal to zero. Its solutions are stationary points of the log-likelihood, and a negative-definite likelihood Hessian identifies an interior local maximum.

A perturbation can have negligible Hellinger distance but a large integral of its density's fourth power. At the uniform density on $[0,1]$, let $b(s)=6s(1-s)$ on $[0,1]$ and zero elsewhere, and let $f_t(u)=1-t^4+t^{-2}b(u/t^6)$. Then $\int f_t=1$ and $\int (\sqrt{f_t}-1{)}^2\le 2t^4=o(t^2)$, so the score function is zero. However $\int f_t^4\ge 72/(35t^2)$. Thus the density fourth-power functional is not continuous along this differentiable-in-quadratic-mean path, despite a bounded baseline and individually bounded nearby densities.

A statistical path is a one-dimensional family of probability measures inside a statistical model, passing through a specified baseline $P$. A differentiability in quadratic mean condition gives its score function, the first-order direction of the family in square-root-density coordinates. The path must respect all restrictions of the statistical model, including normalization and any fixed moments.

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.

The statistical tangent space is the closed linear span in L2 space of a statistical tangent set. Taking this closure makes orthogonal projection available and ensures that a continuous derivative specified on attainable score functions extends to the whole space.