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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 continuous at the chosen target frequency . Let and be as in Regular-grid spatial periodogram and smoothed estimator, and let satisfy Then the denominator defining is positive for all sufficiently large , and 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 , bandwidth sequence, and continuous bounded spectral density in the Statement. For angular representatives define and for define .
Regular-grid spatial periodogram and smoothed estimator: the normalized Fourier-grid weights are and when the denominator is positive.
The Axiom of Choice: the Axiom of Choice is assumed for the imported Bochner representation.
The axiom of dependent choice: a relation in which every element is related to something admits an -indexed chain: Dependent Choice is also assumed for that representation, as in the preceding Fejer theorem.
Fejer convolution formula for the lattice periodogram: the expected raw periodogram is , where and .
Signs, monotonicity intervals, and ranges of sine and cosine and Pi is the first positive zero of sine: sine is increasing on and positive on , so for .
Linearity, monotonicity, and the modulus bound for expectation: expectation passes through the finite weighted sum defining the smoother.
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.
Write , so by [F1]. Fix . If the circular distance of from is at least , choose its representative in ; then by [F2]. The finite geometric-series formula therefore gives By the union bound over coordinates and the unit integral of each factor from [F1], where is the maximum circular distance over the coordinates.
Fix . Continuity of at gives such that whenever . For any with , split the convolution in [F1] into that neighborhood and its complement. On the neighborhood its contribution to is at most because has integral one. On the complement, ; with , step 1.1 gives This bound holds uniformly for all such .
Since is continuous and , there is such that whenever . A nearest Fourier-grid point to is within in each circular coordinate. Since , eventually , 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 implies its grid point is within circular distance of in every coordinate, where contains the support of . Since , all positive-weight grid points eventually lie within the -neighborhood from step 2.1.
For all sufficiently large , [F3] and [A1] give The weights are nonnegative and sum to one, and every positive weight is on a grid point covered by step 2.1. Hence Taking and then arbitrary proves the smoothed bias limit. Taking in step 2.1 proves the raw expectation limit as well.
Depends on
- Regular-grid spatial periodogram and smoothed estimator
- Fejer convolution formula for the lattice periodogram
- The product measure of two sigma-finite measure spaces
- Fubini's theorem for L^1 functions on a sigma-finite product
- Integrable real and complex functions, and their integrals
- Linearity, monotonicity, and the modulus bound for expectation
- Signs, monotonicity intervals, and ranges of sine and cosine
- Pi is the first positive zero of sine
- The Axiom of Choice
- The axiom of dependent choice: a relation in which every element is related to something admits an $\mathbb{N}$-indexed chain
Used by
Cited to discharge well-definedness by Regular-grid spatial periodogram and smoothed estimator.
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Sources
- Evgeny Spodarev, Spatial Statistics: Lecture Notes (2024), Lemmas 2.10 and 2.14 (standard reference, not scraped)