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To perform a first-order substitution, first rename each conflicting bound variable to a fresh name, then replace the designated free occurrences. The fresh names must avoid the formula's relevant free names and all names in the inserted first-order term. For instance, substituting $f(u,t)$ for $z$ in $\exists tQ(z,t)$ requires renaming the binder, producing $\exists sQ(f(u,t),s)$ rather than $\exists tQ(f(u,t),t)$. The latter would change the intended meaning by capturing $t$.

Replacing every free occurrence of variable $z$ in a first-order formula by a first-order term $\tau$. Bound occurrences of $z$ are left alone. The operation requires either that $\tau$ be free for substitution at those occurrences or that conflicting bound variables first be renamed, as in capture-avoiding first-order substitution.

A first-order term $\tau$ is free for substitution for a variable $z$ in a first-order formula when no free variable of $\tau$ becomes bound after replacing a free occurrence of $z$. Equivalently, at every free occurrence to be replaced, none of its enclosing quantifiers binds a variable appearing in $\tau$. The test depends on occurrences and scopes, not merely on which variable names appear anywhere in the formula.

Write $R(v)={\int }_{\mathbb{R}}v(u{)}^{2}du$. The kernel density estimator with bandwidth $h>0$ isSufficient conditions are that $K$ be a nonnegative symmetric probability density function with $R(K)<\infty$ and finite nonzero second moment ${\mu }_2(K)=\int u^{2}K(u)du$, and that $h\to 0$, $nh\to \infty$. Symmetry and the second-moment assumption give $\int uK(u)du=0$. The expectation is a convolution:Taylor's theorem with integral remainder givesThe zeroth-order term integrates to $f(x)$, and the first-order term vanishes. The second derivative is bounded and continuous, so dominated convergence, with dominating function proportional to $u^{2}K(u)$, yieldsCombining this with the supplied leading variance gives the asymptotic mean squared errorWhen $f(x)>0$ and $f^{\prime \prime }(x)\ne 0$, its derivative is $-R(K)f(x)/(n{h}^2)+{\mu }_2(K{)}^2{f}^{\prime \prime }(x{)}^2{h}^3$. It changes sign exactly once, giving the pointwise optimal kernel bandwidthPositivity of $f(x)$ is needed for this positive interior optimum. The printed condition $f^{\prime \prime }(x)\ne 0$ alone does not ensure it. For instance, $f(x)=x^2\varphi (x)$ is a normalized bounded probability density function with bounded continuous square-integrable second derivative, but $f(0)=0$ and $f^{\prime \prime }(0)=2\varphi (0)>0$. At this point the displayed leading variance vanishes and the two-term AMSE has no positive interior minimizer; finer variance terms are needed to determine an appropriate bandwidth. The formal formula there gives zero, which does not satisfy the bandwidth assumptions.

For integrated error, the same expansion holds in $L^2$: translation continuity of $f^{\prime \prime }\in L^2$ applied to the integral remainder givesIndeed Minkowski's integral inequality bounds the norm of the difference, divided by $h^2$, by the integral of $u^{2}K(u){\int }_0^1(1-s)\Vert f^{\prime \prime }(\cdot -shu)-f^{\prime \prime }{\Vert }_{2}dsdu$; each translated difference tends to zero and is bounded by $2\Vert f^{\prime \prime }{\Vert }_2$. This proves the claimed integrated bias expansion without assuming a uniform pointwise remainder over the whole real line.

If $K_h(x)=h^{-1}K(x/h)$, direct integration of the variance givesThe last term is $O(n^{-1})$ because $f$ is bounded and integrable, hence square integrable, and convolution with a probability density function does not increase its $L^2$ norm. It is negligible compared with $1/(nh)$. Thus the asymptotic mean integrated squared error and its minimizer areHere $R(f^{\prime \prime })>0$: otherwise the continuous second derivative vanishes everywhere, making $f$ affine, impossible for a probability density function on the whole real line.

Now take the standard normal density $f=\varphi$. Differentiation gives ${\varphi }^{\prime \prime }(x)=(x^2-1)\varphi (x)$. Gaussian integration givesThe kernel constants cancel from the bandwidth ratio. At $x\ne \pm 1$,Put $t=x^2$. The logarithmic derivative of $e^{t/2}/(t-1{)}^2$ is $1/2-2/(t-1)$. On $0\le t<1$ the function increases, giving its minimum at $t=0$, with value one. On $t>1$ it decreases up to $t=5$ and then increases, giving its minimum $e^{5/2}/16<1$. Thus the global minimizers are $x=\pm \sqrt{5}$, and the normal-density local-to-global bandwidth ratio isThe ratio diverges at the two inflection points $\pm 1$, where the second-order pointwise bias approximation vanishes and needs a higher-order replacement.

Let $R={\prod }_i{\mathcal{M}}_i/F$ be the reduced product by a proper filter on a set. Its underlying equivalence relation is $f\sim g$ when $\{i:f(i)=g(i)\}\in F$. Operations are interpreted coordinatewise, and a relation holds of the classes exactly when its coordinate truth set belongs to $F$.

Evaluation of a first-order term commutes with passage to the quotient, by mathematical induction on terms. Consequently the desired equivalence holds for every atomic formula, including logical equality. For a formula $\theta$ and representatives $\bar{f}$, write $S_{\theta }=\{i:{\mathcal{M}}_i\models \theta (\bar{f}(i))\}$.

For logical conjunction, $S_{\theta \wedge \psi }=S_{\theta }\cap S_{\psi }$. The filter on a set axioms giveThus the induction hypothesis transfers a conjunction in both directions.

For existential quantification, first suppose $R\models \exists y\theta (y,[\bar{f}])$. Choose a representative $g$ of a witness. Induction gives $S_{\theta (g,\bar{f})}\in F$, and this set is contained in $S_{\exists y\theta }$. Upward closure therefore gives $S_{\exists y\theta }\in F$.

Conversely, suppose $A=S_{\exists y\theta }\in F$. For each $i\in A$, choose a coordinate witness $g(i)$, and choose an arbitrary element of $M_i$ outside $A$. These simultaneous choices use the axiom of choice, as does the usual product construction. Then $A\subseteq S_{\theta (g,\bar{f})}$, so that truth set belongs to $F$. Induction gives $R\models \theta ([g],[\bar{f}])$, providing the required witness. Thereforefor every primitive positive formula. The exam's tame formulas are exactly this fragment, built using logical conjunction and existential quantification. No ultrafilter dichotomy was used.