Alphabeta Math
Pipeline-generated
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Spatial summary estimators

1 · Prerequisites

2 · Summary

The Van Hove window and spatial-average claims assume Countable Choice ACω, which makes every Borel window Lebesgue measurable; the boundary excesses are required to be Lebesgue measurable as well. This page defines Van Hove observation windows and their spatial averages, then proves unbiasedness and L2 consistency of the spatial mean when the stationary covariance is absolutely integrable. It defines pair-count lattice covariance and increment-based semivariogram estimators, establishing only their stated unbiasedness results. The regular-grid periodogram is treated as an estimator for the spectral measure of the sampled lattice field on its dual torus. The page makes no variance or distributional claim for the periodogram, and it does not identify the lattice spectrum with an unaliased continuous-space spectrum.

For a field observed at fixed regular-grid spacing, the spectral target is the aliased spectrum on the dual torus. A local sampling identity proves its exact relationship to a continuous-space density. Under AC and DC, suppose the periodized density has a bounded representative continuous at the target and agreeing there with the alias sum. With the kernel hypotheses of Vanishing bias of a smoothed regular-grid spatial periodogram and bandwidths bn→0 and nbn→∞, the expectations of the physically normalized raw and smoothed estimates converge to the unaliased value exactly when every noncentral alias at the target is zero. A continuous density supported in a compact subset of the open Nyquist cube suffices for targets in that cube, as proved in Unaliased fixed-grid spectral bias under the exact no-alias condition. These are expectation limits only.

3 · Logical flowchart

4 · Definitions, theorems and proofs

DefinitionDefinition: Literature-sourcedProof: Not applicableOpen item page →

Van Hove sequences of spatial windows

Definition

Fix an integer d≥1 and assume the Axiom of Countable Choice ACω (The Axiom of Countable Choice (ACω)). By Assuming countable choice, every Borel subset of Rn is Lebesgue measurable, every Borel subset of Rd is Lebesgue measurable under this assumption; by Assuming countable choice, L(Rn) is a sigma-algebra containing every elementary set and λn is a complete measure extending elementary volume, ∣⋅∣ is the resulting complete Lebesgue measure. A Van Hove sequence of spatial windows is a sequence (Wn)n≥0 of bounded Borel sets (The Borel sigma-algebra of a topological space) in Rd with 0<∣Wn∣<∞ for every n, ∣Wn∣→∞, and ∣(Wn+K)∖Wn∣∣Wn∣⟶0for every compact K⊆Rd. Here ∣E∣ is Lebesgue measure when E is Lebesgue measurable, and Wn+K:={x+y:x∈Wn, y∈K}. As part of the condition, each boundary excess (Wn+K)∖Wn in the display is required to be Lebesgue measurable. Thus the window volume diverges while every fixed compact spatial enlargement adds a negligible relative volume.

DefinitionDefinition: Literature-sourcedProof: Not applicableOpen item page →

Spatial window average

Definition

Let (Wn) be a Van Hove sequence in Rd as in Van Hove sequences of spatial windows, under its ACω assumption. The Borel-measurability result makes each Wn Lebesgue measurable, and the complete-measure result supplies the Lebesgue measure used below. On a probability space (Ω,F,P) (Probability measures and probability spaces), let (X(s))s∈Rd be a real random field in the coordinatewise sense of Random fields, stationarity, covariance, and variograms; its coordinates are real random variables (Random elements and real random variables) such that the map (s,ω)↦X(s,ω) is measurable for B(Rd)⊗F (The Borel sigma-algebra of a topological space). For Wn, assume that ∫Wn∣X(s,ω)∣ ds<∞ for almost every ω. Define the spatial window average on that full-measure event by XˉWn(ω):=1∣Wn∣∫WnX(s,ω) ds, and set it to 0 on the measurable exceptional null set. Here ∣Wn∣ and the integral use d-dimensional Lebesgue measure, and a sample path means the function s↦X(s,ω). The theorem Unbiasedness and L2 consistency of the spatial window average ↗ establishes this integrability and measurability from its stated second-moment hypotheses.

TheoremStatement: Literature-sourcedProof: AI-adaptedOpen item page →

Unbiasedness and L2 consistency of the spatial window average

Statement

Assume the Axiom of Countable Choice ACω, as required by Van Hove sequences of spatial windows. Let X(s), s∈Rd, be a jointly measurable real random field on a probability space, with constant mean μ and finite second moments. Assume it is wide-sense stationary in the explicit sense that Cov⁡(X(s+h),X(s))=C(h) for all s,h∈Rd, and that C∈L1(Rd). Let (Wn) be a Van Hove sequence from Van Hove sequences of spatial windows. Then each spatial window average from Spatial window average is well-defined, satisfies E[XˉWn]=μ, and E[∣XˉWn−μ∣2]⟶0. Thus these averages are unbiased and converge to the mean in L2. No variance consistency for any other estimator, weak consistency, or central limit theorem is asserted.

Facts & Assumptions

Given: d≥1, a probability space (Ω,F,P), a jointly measurable real field X:Rd×Ω→R, E[X(s)]=μ, E[X(s)2]<∞ for every s, stationary covariance C(t−s), ∫Rd∣C(h)∣ dh<∞, and a Van Hove sequence (Wn).

[A1]

Van Hove sequences of spatial windows: under ACω, Assuming countable choice, every Borel subset of Rn is Lebesgue measurable makes every Borel Wn Lebesgue measurable and Assuming countable choice, L(Rn) is a sigma-algebra containing every elementary set and λn is a complete measure extending elementary volume supplies Lebesgue measure; every Wn has finite positive volume, ∣Wn∣→∞, and for each compact K, ∣(Wn+K)∖Wn∣/∣Wn∣→0.

[A2]

Spatial window average: when a sample path is Lebesgue integrable, XˉWn=∣Wn∣−1∫WnX(s) ds.

[F1]

Random fields, stationarity, covariance, and variograms, Random elements and real random variables, and The Borel sigma-algebra of a topological space: a field is a family of real random variables on one probability space; joint measurability is an additional hypothesis for spatial integration, and wide stationarity supplies a constant mean and displacement-indexed covariance.

[F2]

Moments, variance, and covariance on a probability space: variance is the expected centered square and covariance is the expected centered product; symmetry makes the reverse-lag convention in the Given equivalent to the supplier covariance convention.

[F3]

Expectation of a nonnegative or integrable random variable: expectation is the Lebesgue integral against P.

[F4]

Cauchy-Schwarz for random variables: for U,V∈L2(P), E∣UV∣≤∥U∥2∥V∥2.

[F5]

The product measure of two sigma-finite measure spaces and Tonelli's theorem for nonnegative measurable functions on a sigma-finite product: nonnegative product- measurable functions can be integrated in either order, and the section integral is measurable.

[F6]

Integrable real and complex functions, and their integrals and Fubini's theorem for L^1 functions on a sigma-finite product: absolutely integrable product-measurable functions have equal iterated and product integrals.

[F8]

Lp convergence for random variables: L2 convergence means that the L2 norm of the difference tends to zero.

[F9]

The product sigma-algebra and its finite iterates, A measurable function between measurable spaces, Every section of a product-measurable function is measurable, and Composition with a Borel measurable outer map preserves measurability: on (Rd×Rd×Ω,(B(Rd)⊗B(Rd))⊗F), the maps ((s,t),ω)↦(s,ω) and ((s,t),ω)↦(t,ω) are measurable because the preimages of generating rectangles A×E are respectively (A×Rd)×E and (Rd×A)×E. Composing with the jointly measurable field shows that (s,t,ω)↦X(s,ω) and (s,t,ω)↦X(t,ω) are measurable, so their centered product is measurable. Also, (s,h)↦s+h is continuous because ∥s+h−(s0+h0)∥2≤∥s−s0∥2+∥h−h0∥2. Every open subset of Rd×Rd is a countable union of rational open rectangles, hence belongs to B(Rd)⊗B(Rd). Thus inverse images of open sets under addition are product-measurable, and the class of sets with product-measurable preimage is a sigma-algebra containing all open sets, hence contains every Borel set. Therefore addition is measurable for the product sigma-algebra and (s,h)↦1Wn(s)1Wn(s+h) is measurable for Borel Wn. Under ACω in [A1], these Borel-measurable functions are also measurable for the product Lebesgue sigma-algebras used in Tonelli. Since C is Lebesgue measurable by C∈L1, its lift (s,h)↦C(h) is measurable for the product Lebesgue sigma-algebra: the preimage of a Lebesgue-measurable set E is Rd×E. Thus C(h)1Wn(s)1Wn(s+h) is product-measurable as well.

[F10]

Dominated convergence: pointwise convergence dominated by an integrable function permits passage of the limit through the integral.

[F11]

Lebesgue measure is sigma-finite, and every metrically bounded subset of Rn has finite outer measure: under the already assumed ACω, Lebesgue measure on Rd is sigma-finite, as witnessed by its increasing finite-measure cubes; its restriction to each bounded measurable Wn is finite.

Proof

Proof technique: establish the exact covariance double integral, then use the Van Hove overlap limit and dominated convergence.

1.1A1A2F1F3F4F5F6F9F11given

By [F11], Lebesgue measure on Rd is sigma-finite under the assumed ACω. Its restriction to Wn is finite by [A1], and the probability measure has total mass 1. Thus all measure factors used below satisfy the sigma-finiteness hypotheses of [F5] and [F6], including the unbounded lag-space factor in Wn×Rd. For every s, [F4] with V=1 gives E∣X(s)∣≤(E[X(s)2])1/2=M<∞, where M is constant by the stated first- and second-order stationarity. The map (s,ω)↦∣X(s,ω)∣ is product-measurable; Tonelli [F5] gives E∫Wn∣X(s)∣ ds=∫WnE∣X(s)∣ ds≤M∣Wn∣<∞. Therefore the sample path is absolutely integrable almost surely. Tonelli also makes the event where this integral is finite measurable; its probability is one. Applying Tonelli separately to X+ and X− makes both section integrals measurable, and their difference on that event, extended by zero on its null complement as in [A2], is measurable.

1.2A2F3F6F11givenalgebra

Since ∫WnE∣X(s)∣ ds<∞, Fubini [F6] applies to X on Wn×Ω. Its constant mean gives E[XˉWn]=1∣Wn∣∫WnE[X(s)] ds=1∣Wn∣∫Wnμ ds=μ.

1.3A2F2F4F5F6F9F11given

Put Z(s):=X(s)−μ. The rectangle-preimage argument in [F9] makes (s,t,ω)↦Z(s,ω)Z(t,ω) product-measurable. By [F4], E∣Z(s)Z(t)∣≤C(0) for all s,t. Iterated Tonelli [F5] therefore gives a finite integral, at most C(0)∣Wn∣2, of the absolute pair product over Wn×Wn×Ω. Each Ω-section is integrable by this bound, so Fubini [F6] makes (s,t)↦E[Z(s)Z(t)] measurable; it equals C(t−s) by [F2]. A second Fubini calculation yields E[(XˉWn−μ)2]=1∣Wn∣2∫Wn∫WnC(t−s) dt ds. This also proves XˉWn∈L2.

1.4A1F5F7F9F11algebra

Define qn(h):=∣Wn∩(Wn−h)∣/∣Wn∣. The addition-measurability argument in [F9] makes (s,h)↦1Wn(s)1Wn(s+h) product-measurable, so Tonelli [F5] gives measurability of its section integral qn. Also 0≤qn(h)≤1. For fixed h, translation invariance [F7] gives ∣Wn∖(Wn−h)∣=∣(Wn+h)∖Wn∣. The latter is bounded by ∣(Wn+{0,h})∖Wn∣, so the Van Hove condition [A1] implies qn(h)→1.

2.1step 1.3F5F6F7F9F11algebra

The product-measurable integrand C(h)1Wn(s)1Wn(s+h) is absolutely integrable: Tonelli and [F9] give ∫Rd∫Wn∣C(h)∣1Wn(s+h) ds dh≤∣Wn∣∥C∥1<∞. In the inner integral of step 1.3 set h=t−s; translation invariance [F7] gives ∫WnC(t−s) dt=∫RdC(h)1Wn(s+h) dh. Fubini [F6] then yields E[(XˉWn−μ)2]=1∣Wn∣2∫RdC(h)∣Wn∩(Wn−h)∣ dh. The overlap factor is the measure of those s∈Wn for which s+h∈Wn.

3.1A1F10step 2.1step 1.4given

Multiplying the formula in step 2.1 by ∣Wn∣ gives ∣Wn∣ E[(XˉWn−μ)2]=∫RdC(h)qn(h) dh. By [F10], the right side converges to ∫C(h) dh because ∣C(h)qn(h)∣≤∣C(h)∣∈L1. It is therefore bounded; since ∣Wn∣→∞, step 2.1 implies E[(XˉWn−μ)2]→0.

4.1F8step 1.2step 3.1∎

The mean identity in step 1.2 and the second-moment limit in step 3.1 are exactly L2 convergence to μ by [F8].

DefinitionDefinition: Literature-sourcedProof: Not applicablejudge pass (gpt-6-luna)Open item page →

Lagged lattice covariance and variogram estimators

Definition

Let d≥1, let (Yj)j∈Zd be a real random field (Random fields, stationarity, covariance, and variograms) whose coordinates are square-integrable real random variables on one probability space (Probability measures and probability spaces, Random elements and real random variables, Moments, variance, and covariance on a probability space, The integers as equivalence classes of pairs of naturals), and let Vn⊂Zd be nonempty finite observation windows. Write Mn:=∣Vn∣ and Yˉn:=1Mn∑j∈VnYj. For a fixed lag h∈Zd, let Pn(h):={j∈Vn:j+h∈Vn},Nn(h):=∣Pn(h)∣. Only indices n with Nn(h)>0 are admissible for this lag.

When the field is wide stationary in the sense of Random fields, stationarity, covariance, and variograms, write its constant mean as μ and its displacement covariance as C(h):=Cov⁡(Yj+h,Yj), which is independent of j by wide stationarity and agrees with the supplier convention by covariance symmetry. Define the pair-count-normalized raw-product covariance estimator by C^n(h):=1Nn(h)∑j∈Pn(h)YjYj+h−Yˉn 2. This convention subtracts the square of the one global sample mean from the pair-count-normalized raw product average; it is not the average of pairwise sample-centered products.

For the second-moment component of order-two intrinsic stationarity in Random fields, stationarity, covariance, and variograms, suppose the increments have finite second moments and γ(h):=12E[(Yj+h−Yj)2] is independent of j; define the empirical semivariogram by γ^n(h):=12Nn(h)∑j∈Pn(h)(Yj+h−Yj)2.

TheoremStatement: Literature-sourcedProof: AI-adaptedOpen item page →

Asymptotic unbiasedness of lattice covariance and variogram estimators

Statement

Let (Yj)j∈Zd be real square-integrable random variables with constant mean E[Yj]=μ and lag-dependent covariance C(h)=Cov⁡(Yj+h,Yj) independent of j. Let (Vn) be nonempty finite observation windows and assume that their sample means from Lagged lattice covariance and variogram estimators satisfy Yˉn→μ in L2. For each fixed h∈Zd with Nn(h)>0 eventually, the pair-count-normalized raw-product covariance estimator satisfies E[C^n(h)]⟶C(h).

Separately, if the second moment of the lag-h increment is independent of j and equals 2γ(h)<∞, then the empirical semivariogram from Lagged lattice covariance and variogram estimators is exactly unbiased: E[γ^n(h)]=γ(h) for every n with Nn(h)>0. These are expectation statements only; they assert no variance consistency, weak consistency, or central limit theorem.

Facts & Assumptions

Given: The lattice field, windows, fixed lag, and L2-consistent sample means in the Statement.

[A1]

Lagged lattice covariance and variogram estimators: for Pn(h)={j∈Vn:j+h∈Vn} and Nn(h)=∣Pn(h)∣, C^n(h)=1Nn(h)∑j∈Pn(h)YjYj+h−Yˉn2, and γ^n(h)=12Nn(h)∑j∈Pn(h)(Yj+h−Yj)2.

[F1]

Moments, variance, and covariance on a probability space: for square-integrable real random variables, covariance is the expectation of the centered product.

[F2]

Linearity, monotonicity, and the modulus bound for expectation: expectation is linear on finite sums of integrable variables, and ∣E[Z]∣≤E[∣Z∣] for integrable Z.

[F3]

Cauchy-Schwarz for random variables: for square-integrable U,V, E[∣UV∣]≤∥U∥2∥V∥2<∞, so UV is integrable.

[F4]

Lp convergence for random variables: Yˉn→μ in L2 means ∥Yˉn−μ∥2→0.

Proof

technique · direct finite-sum expectation calculation
1.1givenF1F3algebra

Every product YjYj+h is integrable by [F3]. By the stationary covariance identity in the Given and [F1], E[YjYj+h]=C(h)+μ2 for every j.

1.2F2F3F4algebra

By [F2], [F3] and [F4], ∣E[Yˉn2]−μ2∣=∣E[(Yˉn−μ)(Yˉn+μ)]∣≤∥Yˉn−μ∥2 ∥Yˉn+μ∥2⟶0, because ∥Yˉn+μ∥22≤2∥Yˉn−μ∥22+8μ2 is bounded by (a+b)2≤2a2+2b2 and [F4].

2.1A1F2step 1.1step 1.2

Applying [F2] to the finite average in [A1] and using step 1.1 gives E[C^n(h)]=C(h)+μ2−E[Yˉn2]. Step 1.2 therefore proves E[C^n(h)]→C(h).

3.1A1F2givenalgebra∎

Under the increment-moment hypothesis, every summand in [A1] has expectation 2γ(h). Linearity [F2] over the Nn(h) pairs and division by 2Nn(h) give E[γ^n(h)]=γ(h) whenever the pair count is positive.

LemmaStatement: Literature-sourcedProof: AI-adaptedjudge pass (gpt-6-luna)Open item page →

The integer lattice is a discrete locally compact abelian group

Statement

For every integer d≥1, equip Zd with coordinatewise addition and its discrete topology. Then it is a locally compact Hausdorff abelian (topological) group. Every function from this discrete space to a topological space is continuous.

Facts & Assumptions

Given: An integer d≥1, coordinatewise addition on Zd, and the discrete topology.

[F1]

The integers form a commutative ring and Group and abelian group: addition on Z is an abelian group operation; in coordinates, the same associativity, commutativity, zero, and inverse laws hold on Zd.

[F4]

Continuity of a map of topological spaces at a point and globally: a map is continuous when its open-set preimages are open.

[F5]

Topological group: multiplication and inversion are continuous: a group with a topology is a topological group when multiplication and inversion are continuous.

[F8]

Proof

technique · verify the algebraic and topological properties directly
1.1F1givenalgebra

The additive group of Z is abelian by [F1]. Coordinatewise addition on Zd is associative and commutative, has the zero vector as identity, and sends x to −x coordinatewise as its inverse. Hence Zd is an abelian group.

1.2F2F6F7F8given

For distinct x,y∈Zd, the singleton sets {x} and {y} are disjoint open neighbourhoods by [F2], so the space is Hausdorff by [F6]. Each singleton {x} is finite and hence compact by [F7]; it is an open neighbourhood of x by [F2]. Thus the space is locally compact by [F8].

2.1F2F3F4F5step 1.1step 1.2given∎

The product topology on Zd×Zd is discrete: for each pair (x,y), the basic open set {x}×{y} is its singleton by [F2, F3]. Therefore coordinatewise addition Zd×Zd→Zd and inversion Zd→Zd are continuous by [F4]. By [F5] the group is a topological group, and with step 1.2 it is locally compact Hausdorff abelian. Every map from Zd is continuous by [F2, F4].

LemmaStatement: Literature-sourcedProof: AI-adaptedjudge pass (gpt-6-luna)Open item page →

The Pontryagin dual of the integer lattice is the torus

Statement

Give Zd the discrete topology and let T:={z∈C:∣z∣=1} be the multiplicative unit circle. The map Ψ:Td⟶Zd^,Ψ(z1,…,zd)(k1,…,kd):=∏r=1dzrkr is a topological group isomorphism, where the dual has the compact-open topology and Td has the product topology.

Facts & Assumptions

Given: d≥1, the discrete additive group Zd, the multiplicative unit circle T, and the map Ψ in the Statement.

[F1]

The Pontryagin dual with the compact-open topology: a character is a continuous group homomorphism to T, and the dual consists of these characters with pointwise multiplication and the compact-open topology.

[F2]

On a discrete domain the compact-open topology is the topology of pointwise convergence: on a discrete domain, the compact-open topology on the character set is the subspace topology inherited from TZd; in particular, evaluation at any fixed lattice point is continuous.

[F5]

Homeomorphism, open map, closed map, embedding, and what it means for a property to be topological: a homeomorphism is a bijection that is continuous with continuous inverse.

[F6]

The integer lattice is a discrete locally compact abelian group: Zd with coordinatewise addition and the discrete topology is a Hausdorff locally compact abelian topological group; maps out of it are continuous.

Verification

1.1F1F3F6

For z=(z1,…,zd)∈Td, the formula for Ψ(z) is a homomorphism because for k,ℓ∈Zd, Ψ(z)(k+ℓ)=∏rzrkr+ℓr=(∏rzrkr)(∏rzrℓr). It is continuous because its domain Zd is discrete, so every preimage of an open set is open. Therefore Ψ(z)∈Zd^.

1.2F1F3F4F6

The map Ψ is a group homomorphism because Ψ(zz′)(k)=∏r(zrzr′)kr=(∏rzrkr)(∏r(zr′)kr) by [F3] and commutativity of T [F4]. If χ∈Zd^, put zr:=χ(er), where er is the rth standard basis vector. Then zr∈T by [F1], and the decomposition k=∑rkrer together with homomorphism and power laws gives χ(k)=∏rzrkr. Thus χ=Ψ(z); also zr=χ(er) recovers each coordinate uniquely, so Ψ is bijective.

1.3F2F4

For each fixed k, the coordinate map z↦Ψ(z)(k) is a finite product of continuous integer-power maps on T, hence continuous by [F4]. The product-topology criterion and [F2] then imply that Ψ is continuous into Zd^.

2.1F1F2F5step 1.2step 1.3∎

The inverse map is χ↦(χ(e1),…,χ(ed)). Each coordinate is continuous by [F2], so the inverse is continuous into the finite product Td. Thus Ψ is a homeomorphism by [F5] and a group isomorphism by step 1.2 and the pointwise group law in [F1].

LemmaStatement: AI-adaptedProof: AI-generatedjudge pass (gpt-6-luna)Open item page →

Transport of a lattice spectral measure to torus coordinates

Statement

Let Ψ:Td→Zd^ be the homeomorphism from The Pontryagin dual of the integer lattice is the torus, and let μ be a finite positive Radon measure on Zd^. Define F(B):=μ(Ψ(B)) for every Borel set B⊆Td. Then F is a finite positive Radon measure on Td, and for every bounded Borel function g:Td→C, ∫Tdg(θ) F(dθ)=∫Zd^g(Ψ−1(χ)) μ(dχ).

Facts & Assumptions

Given: The homeomorphism Ψ and the finite positive Radon measure μ from the Statement.

[F1]

The Pontryagin dual of the integer lattice is the torus: Ψ is a homeomorphism, so it and its inverse preserve Borel measurability, compactness and openness.

[F2]

Radon measure on an LCH space: a Radon measure is finite on compact sets, outer regular on Borel sets and inner regular on open sets.

[F3]

The Lebesgue integral is linear on L1(μ), Every nonnegative measurable function is the increasing limit of simple measurable functions, and Monotone convergence for the integral: integrals of simple functions are linear, every nonnegative measurable function is the increasing limit of nonnegative measurable simple functions, and increasing limits pass through the integral.

[F5]

Integrable real and complex functions, and their integrals: bounded Borel functions are integrable against the finite measures in the Statement.

Proof

Proof technique: define the transported measure and extend its integral identity from indicators.

1.1F1given

If B1,B2,… are pairwise disjoint Borel subsets of Td, then their images under the bijection Ψ are pairwise disjoint Borel sets by [F1]. Hence F ⁣(⋃m≥1Bm)=μ ⁣(⋃m≥1Ψ(Bm))=∑m≥1μ(Ψ(Bm))=∑m≥1F(Bm). Thus F is a positive measure, and F(Td)=μ(Zd^)<∞.

1.2F1F2F4given

For Borel B⊆Td and ε>0, outer regularity of μ gives an open U⊇Ψ(B) with μ(U)<μ(Ψ(B))+ε. Then Ψ−1(U) is open, contains B, and F(Ψ−1(U))=μ(U), proving outer regularity of F. If U is open in Td, inner regularity of μ gives compact C⊆Ψ(U) with μ(C)>μ(Ψ(U))−ε. Then Ψ−1(C) is compact in U and F(Ψ−1(C))=μ(C), proving inner regularity on open sets. The torus is compact Hausdorff by [F4], so these properties make F Radon as in [F2].

2.1F1F3F5algebra∎

For an indicator g=1B of a Borel set, ∫g dF=F(B)=μ(Ψ(B))=∫1B(Ψ−1(χ)) μ(dχ). Finite linearity [F3] extends this equality to Borel simple functions. For a nonnegative Borel function g, the simple-approximation theorem [F3] supplies Borel simple functions sm↑g. Since Ψ−1 is continuous, sm∘Ψ−1↑g∘Ψ−1 measurably on the dual. Applying monotone convergence [F3] to the simple-function identity on both spaces gives the asserted identity for g. Splitting the real and imaginary parts of a bounded complex Borel function into positive and negative parts then gives the stated identity by [F5].

DefinitionDefinition: Literature-sourcedProof: Not applicablejudge pass (gpt-6-luna)Open item page →

Regular-grid spatial periodogram and smoothed estimator

Definition

Let Y=(Yj)j∈Zd satisfy the constant-mean, lag-dependent-covariance conditions from Lagged lattice covariance and variogram estimators, with known mean μ (set μ=0 for a centered field), and use the frequency torus Td identified with Zd^ by The Pontryagin dual of the integer lattice is the torus. Represent frequency coordinates λ∈[−π,π)d by the circle tuple (eiλ1,…,eiλd); coordinates and integration are periodic modulo 2π in each coordinate. For n≥1, set Λn:={0,1,…,n−1}d,Nn:=nd, and let the nd Fourier-grid frequencies be λn,q:=2πq/n(mod2π),q∈Λn. The known-mean regular-grid periodogram is In(λ):=1(2π)dNn∣∑j∈Λn(Yj−μ)e−ij⋅λ∣2,λ∈Td. Integration over Td uses product angular Lebesgue measure on the fundamental cube [−π,π)d (The product measure of two sigma-finite measure spaces).

For smoothing, let K:Rd→[0,∞) be continuous, even, compactly supported, and satisfy K(0)>0. For b>0, periodize the rescaled kernel by Kbper(x):=∑m∈Zdb−dK((x+2πm)/b). Because K has compact support, only finitely many summands are nonzero for each fixed x, so this periodization is well-defined. For λ∈Td, when the denominator is positive, set wn,q,b(λ):=Kbper(λ−λn,q)∑r∈ΛnKbper(λ−λn,r),f~n,b(λ):=∑q∈Λnwn,q,b(λ)In(λn,q). The smoother is undefined when that denominator vanishes. Its target, when a spectral density exists, is the density of the lattice-sampled field on the dual torus; no unaliased continuous-space spectral-density target is part of this definition.

TheoremStatement: Literature-sourcedProof: AI-adaptedjudge pass (gpt-6-luna)Open item page →

Fejer convolution formula for the lattice periodogram

Statement

Let d≥1 and let (Yj)j∈Zd be real square-integrable random variables with constant mean μ and lag-dependent covariance c(h):=Cov⁡(Yj+h,Yj), independent of j. Assume the Axiom of Choice and Dependent Choice. Then there is a unique finite positive Radon measure F on the angular torus Td such that c(h)=∫Tdeih⋅θ F(dθ),h∈Zd. For the periodogram In of Regular-grid spatial periodogram and smoothed estimator, define the product Fejer kernel Φn(x):=1(2π)dnd∣∑j∈{0,…,n−1}de−ij⋅x∣2=∏r=1d12πn∣∑a=0n−1e−iaxr∣2. Then, for every n≥1 and λ∈Td, E[In(λ)]=∫TdΦn(λ−θ) F(dθ). The kernel is nonnegative and has integral one with respect to product angular Lebesgue measure. The spectral measure and density here belong to the lattice-indexed observations on the dual torus.

Facts & Assumptions

Given: The lattice field, constant mean, covariance convention, and periodogram definition in the Statement; assume AC and DC.

[A1]

The Axiom of Choice: the Axiom of Choice is assumed.

[F1]

Positive definite functions on an abelian group: a function c:G→C is positive definite when every finite quadratic form ∑r,saras‾ c(xr−xs) is nonnegative.

[F2]

Bochner's theorem for LCA groups: under AC and DC, every continuous positive-definite function on an LCA group has a unique finite positive Radon representing measure on its Pontryagin dual.

[F3]

The Pontryagin dual of the integer lattice is the torus: Zd^ is identified with the product torus by χθ(j)=eij⋅θ.

[F4]

Transport of a lattice spectral measure to torus coordinates: a finite positive Radon measure on Zd^ transports to a finite positive Radon measure on Td while preserving integrals after composition.

[F5]

Regular-grid spatial periodogram and smoothed estimator: for Λn={0,…,n−1}d and Nn=nd, In(λ)=1(2π)dNn∣∑j∈Λn(Yj−μ)e−ij⋅λ∣2.

[F6]

Moments, variance, and covariance on a probability space: covariance is the expected centered product of square-integrable real random variables.

[F7]

Linearity, monotonicity, and the modulus bound for expectation and Cauchy-Schwarz for random variables: expectation is linear on finite integrable sums, and products of square-integrable variables are integrable.

[F8]

The product measure of two sigma-finite measure spaces and Fubini's theorem for L^1 functions on a sigma-finite product: the product angular Lebesgue measure exists and integrals can be evaluated by sections.

[F9]

The integer lattice is a discrete locally compact abelian group: Zd with the discrete topology is Hausdorff, abelian and locally compact, and every function on it is continuous.

[F10]

Integer exponentials are orthogonal for angular Lebesgue measure: under Countable Choice, for every integer k, ∫−ππeikx dx is 2π when k=0 and zero otherwise.

[F11]

AC implies DC implies countable choice: AC implies Countable Choice (through DC).

[F12]

The Axiom of Countable Choice (ACω): Countable Choice selects one element from each set in a countable family of nonempty sets.

Proof

Proof technique: prove the covariance sequence is positive definite, apply Bochner on the discrete lattice, then expand the finite Fourier square.

1.1F1F6F7givenalgebra

For any finite lattice points k1,…,km∈Zd and complex coefficients a1,…,am, the finite sum ∑rar(Ykr−μ) is square-integrable. Expanding its squared modulus, using [F6] and stationarity, gives ∑r,saras‾c(kr−ks)=E∣∑rar(Ykr−μ)∣2≥0. Thus c is positive definite by [F1].

1.2A1A2F2F9given

By [F9], Zd is a locally compact Hausdorff abelian group and every function on it is continuous, so c is continuous. By Bochner's theorem [F2], using AC [A1] and DC [A2], there is a unique finite positive Radon measure F0 on Zd^ such that c(h)=∫Zd^χ(h) F0(dχ).

1.3F5F6F7givenalgebra

Expand the finite squared modulus in [F5]. Every product is integrable by [F7], and stationarity gives E[In(λ)]=1(2π)dnd∑j,k∈Λnc(j−k)e−i(j−k)⋅λ.

2.1F3F4step 1.2

By [F3] and [F4], transport F0 to the unique finite positive Radon measure F on Td. Applying the integral-transport identity [F4] to χ↦χ(h) gives c(h)=∫Tdeih⋅θ F(dθ).

3.1F5step 2.1step 1.3F7algebra

Substitute the spectral representation from step 2.1 into each summand of step 1.3. Linearity permits moving the finite sum through the integral, and ∑j,k∈Λne−i(j−k)⋅(λ−θ)=∣∑j∈Λne−ij⋅(λ−θ)∣2. After dividing by (2π)dnd this is precisely E[In(λ)]=∫TdΦn(λ−θ) F(dθ).

4.1A1F8F10F11F12algebra∎

The displayed kernel factors into nonnegative one-dimensional kernels. For each coordinate, expanding the finite square and applying angular orthogonality [F10] to each integer exponent a−b gives ∫−ππ12πn∣∑a=0n−1e−iax∣2dx=1. The Countable Choice hypothesis of [F10] follows from AC by [F11] and [A1]. Fubini [F8] for the finite product then gives ∫TdΦn(x) dx=1, and nonnegativity follows from the squared modulus.

LemmaStatement: AI-adaptedProof: AI-generatedjudge pass (gpt-6-luna)Open item page →

Spectral alias identity for fixed regular-grid sampling

Statement

Assume AC and DC. Let X(s), s∈Rd, be a real wide-stationary field with mean μ and covariance admitting the density representation C(h)=∫Rdeih⋅ωf(ω) dω,f:Rd→[0,∞),f∈L1(Rd), where f is a specified Borel representative. Fix spacing δ>0 and sample Yj=X(δj), j∈Zd. On Q=[−π,π)d set gδ(θ):=δ−d∑k∈Zdf((θ+2πk)/δ). This nonnegative measurable sum is finite almost everywhere, is in L1(Q), and defines a periodic density of the sampled field's unique torus spectral measure: c(h)=C(δh)=∫Qeih⋅θgδ(θ) dθ,∫Qgδ=∫Rdf=C(0). Thus for the angular periodogram of Fejer convolution formula for the lattice periodogram, E[In(λ)]=∫QΦn(λ−θ)gδ(θ) dθ=∫RdΦn(λ−δω)f(ω) dω. The general fixed-grid target is this aliased density, without any alias-free assumption. Where a pointwise density limit is claimed, a version continuous at the target must be specified; a spectral measure alone supplies no canonical pointwise density value.

Facts & Assumptions

Given: AC and DC, the wide-stationary continuous-space field with its specified integrable nonnegative density, and fixed regular-grid spacing δ>0.

[F1]

Random fields, stationarity, covariance, and variograms: wide stationarity gives a constant mean and displacement covariance; restricting to sites δj preserves these properties.

[F5]

The multiplicative unit circle is a compact metrizable topological abelian group, A product of finitely many compact spaces is compact in the product topology, and Locally finite Borel measures on second-countable LCH spaces are regular: the finite-dimensional torus is compact Hausdorff and second-countable, and under Countable Choice its finite Borel measures are Radon. A countable base is obtained by intersecting rational Euclidean balls with its realization in R2d.

[F6]

Fejer convolution formula for the lattice periodogram: under AC and DC a stationary lattice covariance has a unique finite positive Radon torus spectral measure, and EIn is its Fejer convolution.

[F8]

Every nonnegative measurable function is the increasing limit of simple measurable functions: every nonnegative measurable function is the increasing pointwise limit of nonnegative measurable simple functions.

[F9]

The nonnegative Lebesgue integral: the nonnegative integral of a measurable function is the supremum of the integrals of its nonnegative simple minorants; for a simple function its integral is the finite sum over its disjoint level sets.

Proof

1.1F2F3F7F8givenalgebra

First establish the countability needed for the sum. By [F7], the quotient map π:N×N→Z is surjective, and β:N→N×N is bijective. Their composite is onto: for any class z, take a representative (a,b) and the unique j with β(j)=(a,b); then π(β(j))=z. Thus Z is at most countable by the surjection criterion in [F7]. Its finite power Zd is at most countable by [F7] and is nonempty, since it contains the constant tuple with value [(0,0)] (and for d=0 the empty tuple). The same criterion supplies a surjection e:N→Zd. Put Km:={e(j):0≤j≤m}. These are increasing finite subsets whose union is Zd. For a nonnegative family (ak) define its sum as lim⁡m∑k∈Kmak. Each index is counted once, even if e repeats values. Every finite subset of Zd is contained in some Km: choose a preimage index for each of its finitely many elements and take their maximum. Hence the limit equals the supremum of all finite-subset sums and is invariant under reindexing. No countable family of choices is required. Each θ↦f((θ+2πk)/δ) is nonnegative Borel. The finite partial sums over Km are Borel and increase; for each real a, the strict superlevel set of their limit is the union of their Borel strict superlevel sets, so the limit is measurable. Reindexing the sum over finite subsets shows gδ is periodic. The translates Q+2πk, k∈Zd, form a disjoint partition of Rd. Put Pm:=⋃k∈Km(Q+2πk), so Pm↑Rd. Translation invariance [F2] first for indicators, then for simple functions by [F9] and by monotone convergence [F3] for nonnegative Borel functions, gives the cellwise integral translations. Monotone convergence [F3] then gives ∫Qgδ(θ)dθ=δ−dlim⁡m∑k∈Km∫Q+2πkf(u/δ)du=δ−d∫Rdf(u/δ)du. For the dilation identity, [F2] gives it first for indicators of Borel sets. For a nonnegative simple function written over disjoint level sets, summing these indicator identities gives the identity for the simple function by [F9]. By [F8], every nonnegative Borel function is an increasing limit of simple functions; monotone convergence [F3] therefore extends the dilation identity to every such function, including f. Thus ∫Qgδ(θ)dθ=∫Rdf(ω)dω<∞. The measurable set where gδ=∞ is null: if it had positive measure, the integral would be at least M times that measure for every M>0, contradicting finiteness. This set is periodic, so a finite-valued representative obtained by replacing gδ by zero there remains periodic and defines the same measure.

2.1F1F2F3F4F5F6step 1.1algebra

For integer h, the identity eih⋅(θ+2πk)=eih⋅θ and the finite mass in step 1.1 permit the same increasing finite-union partition for the absolutely integrable complex integrand. Translation on each cell, using the integral- translation identity established in step 1.1 on the nonnegative parts of its real and imaginary parts, gives ∫Qeih⋅θgδ(θ)dθ=δ−d∫Rdeih⋅uf(u/δ) du. To apply dilation to this complex integral, write eih⋅uf(u/δ) as the differences of its nonnegative cosine and sine parts. Apply the nonnegative scaling identity established in step 1.1 to f(ω)max⁡(cos⁡(δh⋅ω),0), f(ω)max⁡(−cos⁡(δh⋅ω),0), and the corresponding two sine parts. Each is Borel and bounded by f, hence integrable; recombining their integrals yields ∫Qeih⋅θgδ(θ)dθ=∫Rdeiδh⋅ωf(ω)dω=C(δh)=c(h). The density defines a finite Borel measure on the torus, Radon by [F5] and [F4]. By [F1] the sample is a stationary lattice field; symmetry of real covariance identifies Cov⁡(Yj+h,Yj) with C(δh). Uniqueness in [F6] identifies that measure with its torus spectrum. The mass identity at h=0 is C(0).

3.1F2F3F6step 1.1step 2.1algebra∎

Apply [F6] to the sampled spectrum in step 2.1 to obtain the first periodogram expectation identity. Since Φn is periodic, bounded for each fixed n, and nonnegative, partitioning the density integral exactly as in step 1.1 gives its second expression over Rd. That expression integrates a periodic kernel against the original density and does not remove the noncentral translates.

Source notes

Spodarev's finite-grid calculation in Lemma 2.10(b) produces the last periodic-kernel integral. Its subsequent unconditional claim of convergence to the unaliased continuous-space density omits periodization. The local identity supplies the correct target; the alias-free bias result is a separate corollary with an explicit exact condition.

TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-6-luna)Open item page →

Vanishing bias of a smoothed regular-grid spatial periodogram

Statement

Assume the Axiom of Choice and Dependent Choice, as in Fejer convolution formula for the lattice periodogram. Let the lattice spectral measure from that theorem be absolutely continuous with a specified bounded representative f∈L∞(Td) continuous at the chosen target frequency λ0∈Td. Let K and f~n,b be as in Regular-grid spatial periodogram and smoothed estimator, and let bn>0 satisfy bn→0,nbn→∞. Then the denominator defining f~n,bn(λ0) is positive for all sufficiently large n, and E[In(λ0)]⟶f(λ0),E[f~n,bn(λ0)]⟶f(λ0). This is an asymptotic-bias statement only; it asserts no variance limit, weak consistency, or central limit theorem.

Facts & Assumptions

Given: The known-mean regular-grid periodogram, periodized kernel Kbper, bandwidth sequence, and continuous bounded spectral density in the Statement. For angular representatives define ρT(x,y):=min⁡m∈Z∣x−y+2πm∣,ρ∞(λ,θ):=max⁡1≤r≤dρT(λr,θr), and for u∈Rd define ∥u∥∞:=max⁡r∣ur∣.

[A1]

Regular-grid spatial periodogram and smoothed estimator: the normalized Fourier-grid weights are wn,q,b(λ) and f~n,b(λ)=∑qwn,q,b(λ)In(λn,q) when the denominator is positive.

[A2]

The Axiom of Choice: the Axiom of Choice is assumed for the imported Bochner representation.

[A3]

The axiom of dependent choice: a relation in which every element is related to something admits an N-indexed chain: Dependent Choice is also assumed for that representation, as in the preceding Fejer theorem.

[F1]

Fejer convolution formula for the lattice periodogram: the expected raw periodogram is gn(λ):=E[In(λ)]=∫TdΦn(λ−θ)f(θ) dθ, where Φn≥0 and ∫TdΦn=1.

[F2]

Signs, monotonicity intervals, and ranges of sine and cosine and Pi is the first positive zero of sine: sine is increasing on [0,π/2] and positive on (0,π), so sin⁡(δ/2)>0 for 0<δ<π.

[F3]

Linearity, monotonicity, and the modulus bound for expectation: expectation passes through the finite weighted sum defining the smoother.

[F4]

The product measure of two sigma-finite measure spaces and Fubini's theorem for L^1 functions on a sigma-finite product: product angular measure and iterated integrals for the product Fejer kernel use the stated Lebesgue product measure.

Proof

Proof technique: control the Fejer mass outside neighborhoods, then use the kernel support and Fourier-grid mesh to localize the smoothing weights.

1.1A2A3F1F2F4givenalgebra

Write ϕn(x):=(2πn)−1∣∑a=0n−1e−iax∣2, so Φn(x)=∏r=1dϕn(xr) by [F1]. Fix 0<δ<π. If the circular distance of x from 0 is at least δ, choose its representative in [−π,π]; then ∣sin⁡(x/2)∣≥sin⁡(δ/2)>0 by [F2]. The finite geometric-series formula therefore gives ϕn(x)=12πn∣sin⁡(nx/2)sin⁡(x/2)∣2≤12πnsin⁡2(δ/2). By the union bound over coordinates and the unit integral of each factor from [F1], ∫{x:ρ∞(x,0)≥δ}Φn(x) dx≤dnsin⁡2(δ/2), where ρ∞ is the maximum circular distance over the d coordinates.

2.1A2A3F1step 1.1given

Fix ε>0. Continuity of f at λ0 gives 0<δ<π such that ∣f(θ)−f(λ0)∣<ε whenever ρ∞(θ,λ0)<2δ. For any λ′ with ρ∞(λ′,λ0)<δ, split the convolution in [F1] into that neighborhood and its complement. On the neighborhood its contribution to ∣gn(λ′)−f(λ0)∣ is at most ε because Φn has integral one. On the complement, ρ∞(λ′−θ,0)≥δ; with M:=∥f∥∞+∣f(λ0)∣<∞, step 1.1 gives ∣gn(λ′)−f(λ0)∣≤ε+Mdnsin⁡2(δ/2). This bound holds uniformly for all such λ′.

3.1A1givenstep 2.1algebra

Since K is continuous and K(0)>0, there is a>0 such that K(u)>0 whenever ∥u∥∞<a. A nearest Fourier-grid point to λ0 is within π/n in each circular coordinate. Since nbn→∞, eventually π/(nbn)<a, so the periodized kernel is positive at that grid point and the denominator in [A1] is positive. Moreover, if a weight in [A1] is positive, compact support of K implies its grid point is within circular distance Rbn of λ0 in every coordinate, where [−R,R]d contains the support of K. Since bn→0, all positive-weight grid points eventually lie within the δ-neighborhood from step 2.1.

4.1A1A2A3F3step 2.1step 3.1algebra∎

For all sufficiently large n, [F3] and [A1] give E[f~n,bn(λ0)]=∑q∈Λnwn,q,bn(λ0)gn(λn,q). The weights are nonnegative and sum to one, and every positive weight is on a grid point covered by step 2.1. Hence ∣E[f~n,bn(λ0)]−f(λ0)∣≤ε+Mdnsin⁡2(δ/2). Taking n→∞ and then arbitrary ε↓0 proves the smoothed bias limit. Taking λ′=λ0 in step 2.1 proves the raw expectation limit as well.

CorollaryStatement: AI-adaptedProof: AI-generatedjudge pass (gpt-6-luna)Open item page →

Unaliased fixed-grid spectral bias under the exact no-alias condition

Statement

Assume the model and AC/DC of Spectral alias identity for fixed regular-grid sampling. Fix ω0∈Rd, and put λ0=δω0 modulo 2π. Suppose the pointwise periodized sum gδ has a bounded torus representative continuous at λ0, agreeing with that sum at λ0. Use the kernel and bandwidths of Vanishing bias of a smoothed regular-grid spatial periodogram. Define the physically normalized estimates at ω0 by f^nraw(ω0)=δdIn(δω0),f^nsm(ω0)=δdf~n,bn(δω0). Then the smoother is defined eventually and both expectations converge to aδ(ω0):=∑k∈Zdf(ω0+2πk/δ). In particular, both have vanishing bias for the unaliased value f(ω0) if and only if f(ω0+2πk/δ)=0for every k≠0. This exact pointwise condition does not restrict the general aliased-target theorem. A sufficient global condition is that f is continuous and supported in a compact subset of the open Nyquist cube (−π/δ,π/δ)d, with ω0 in that cube. No variance, consistency or CLT conclusion is asserted.

Facts & Assumptions

Given: The fixed-spacing continuous-space model, a bounded periodized density representative continuous at the target and agreeing there with its sum, the kernel/bandwidth assumptions, and AC/DC.

[F1]

Spectral alias identity for fixed regular-grid sampling: gδ is the torus density of the sampled field and its pointwise sum is δ−d∑kf((λ+2πk)/δ).

[F2]

Vanishing bias of a smoothed regular-grid spatial periodogram: for a bounded torus density continuous at the chosen target, both raw and smoothed periodogram expectations converge to that density value; smoothing denominators are eventually positive.

[F3]

Regular-grid spatial periodogram and smoothed estimator: the normalized Fourier-grid smoother is the specified finite nonnegative weighted sum of angular periodogram ordinates.

[A1]

The Axiom of Choice and The axiom of dependent choice: a relation in which every element is related to something admits an N-indexed chain: AC and DC are inherited through the lattice spectral representation and its bias theorem.

Proof

1.1A1F1F2F3givenalgebra

By [F1] the sampled field has torus density gδ, and the assumed representative meets all hypotheses of [F2]. Thus the denominator in [F3] is eventually positive and both angular expectations tend to gδ(λ0). Multiplying by δd and reindexing the convergent nonnegative sum at the target gives δdgδ(λ0)=∑kf(ω0+2πk/δ)=aδ(ω0). This proves the general aliased limit for both physically normalized estimates.

2.1F1step 1.1givenalgebra∎

Every alias summand is nonnegative. Therefore aδ(ω0)−f(ω0)=∑k≠0f(ω0+2πk/δ) is zero if and only if every summand is zero. This proves both directions of the stated exact condition. Under the sufficient global condition, only the central translate is nonzero for frequencies in the Nyquist cube; its support stays away from the boundary. Hence the periodic density is bounded and continuous across the boundary as well, and has no noncentral alias at the stated target. All hypotheses and the exact condition are then verified.

5 · Examples, counterexamples and false statements

None yet.

Sources