# Spectral Density Estimation for Random Fields via Periodic Embeddings

arXiv: https://arxiv.org/abs/1710.08978 · HTML: https://mlsanity.com/paper/1710.08978/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2017.

## Built on by (most cited first, 2 of 2)

- [MuyGPs: Scalable Gaussian Process Hyperparameter Estimation Using Local Cross-Validation](https://mlsanity.com/paper/2104.14581/) (2021)
- [Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation](https://mlsanity.com/paper/2205.10879/) (2022)

## Abstract (arXiv)

We introduce methods for estimating the spectral density of a random field on a d-dimensional lattice from incomplete gridded data. Data are iteratively imputed onto an expanded lattice according to a model with a periodic covariance function. The imputations are convenient computationally, in that circulant embedding and preconditioned conjugate gradient methods can produce imputations in O(nlog n) time and O(n) memory. However, these so-called periodic imputations are motivated mainly by their ability to produce accurace spectral density estimates. In addition, we introduce a parametric filtering method that is designed to reduce periodogram smoothing bias. The paper contains theoretical results studying properties of the imputed data periodogram and numerical and simulation studies comparing the performance of the proposed methods to existing approaches in a number of scenarios. We present an application to a gridded satellite surface temperature dataset with missing values.