Large Signal ModelReading edition

64. A valid density approximation with a resolution bound

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64. A valid density approximation with a resolution bound

Exact coarsening gives a finite event model. To compare complete continuous observation laws, lift that model back to a density on the same observation space. Assume all cells have positive reference mass and put

\[ B_a^\Pi(y)=\frac{B_{a,j}}{\mu(A_j)}\quad(y\in A_j). \]

This uses the normalized reference measure inside each cell. It is an explicit density approximation, not an assertion that the original conditional density was constant there.

Proposition 58: Positive cell-average approximation

The family \(B_a^\Pi\) is cone preserving and mass conserving. It has exactly the same cell operators as \(B_a\). Let \(Y\) have a metric and suppose

\[ \|B_a(y)-B_a(v)\|_{C\to C}\leq L\,d(y,v) \]

in the induced base norm, with every cell of diameter at most \(h\). Then

\[ \epsilon_\Pi:=\sup_{a,z\in K}\int \|(B_a(y)-B_a^\Pi(y))z\|_C\,\mu(dy) \leq Lh. \]

For equal intervals of width \(h\) under the uniform measure on \([-1,1]\), the sharper bound is \(\epsilon_\Pi\leq Lh/3\).

Proof

Each cell average is a positive scalar multiple of a positive cell operator. Its integral over that cell is \(B_{a,j}\), proving both normalization and exact cell agreement. For \(y\in A_j\), subtract the cell average and use

\[ \|(B_a(y)-B_a^\Pi(y))z\|_C \leq\frac{L}{\mu(A_j)}\int_{A_j}d(y,v)\,\mu(dv), \]

because \(\|z\|_C=1\). Integrating over \(y\) proves the diameter bound. Two independent uniform points in an interval of width \(h\) have mean absolute separation \(h/3\), obtained by integrating over its two triangles. Applying this inside every equal interval gives the sharper bound. \(\square\)

The Lipschitz constant is a declared operator bound. It is not a fit residual inferred from adjacent samples. The comparison needs a common domain and a common reference measure. If two kernels are only close in Euclidean coefficient norm, the relevant norm-conversion constant must be included.

In the full \(2\times2\) symmetric-matrix example, the pullback base norm is

\[ \|(m,x,y)\|_C=\max\{|m|,\sqrt{x^2+y^2}\}=\|\mathcal J(m,x,y)\|_1. \]

There \(\|K(u)\|_2\leq2\) and \(\|K(u)-K(v)\|_2\leq(3/2)|u-v|\). The identity

\[ K(u)XK(u)^{\mathsf T}-K(v)XK(v)^{\mathsf T} =(K(u)-K(v))XK(u)^{\mathsf T} +K(v)X(K(u)-K(v))^{\mathsf T} \]

and \(\|AXB\|_1\leq\|A\|_2\|X\|_1\|B\|_2\) give \(L=6\). The rotated action uses an orthogonal matrix before this map and has the same bound. Equal bins therefore give \(\epsilon_\Pi\leq4/n\), capped by two for two valid instruments. The inequality is sufficient, not asserted sharp.

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