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空间混杂下高斯随机场回归斜率的填充渐近一致可估性AI 解读

The problem of estimating the slope parameter in regression between two spatial processes under confounding by an unmeasured spatial process has received widespread attention in the recent statistical literature. Yet, a fundamental question remains unresolved: when is this slope consistently estimable under spatial confounding, with existing insights being largely empirical or estimator-specific. In this manuscript, we characterize conditions for consistent estimability of…

高斯随机场的线性计算成本协方差函数AI 解读

Gaussian random fields (GRF) are a fundamental stochastic model for spatiotemporal data analysis. An essential ingredient of GRF is the covariance function that characterizes the joint Gaussian distribution of the field. Commonly used covariance functions give rise to fully dense and unstructured covariance matrices, for which required calculations are notoriously expensive to carry out for large data. In this work, we propose a construction of covariance functions that…

When using optimal linear prediction to interpolate point observations of a mean square continuous stationary spatial process, one often finds that the interpolant mostly depends on those observations located nearest to the predictand. This phenomenon is called the screening effect. However, there are situations in which a screening effect does not hold in a reasonable asymptotic sense, and theoretical support for the screening effect is limited to some rather specialized…

用于卫星数据同化的集合卡尔曼滤波与平滑方法AI 解读

This paper proposes a methodology for combining satellite images with advection-diffusion models for interpolation and prediction of environmental processes. We propose a dynamic state-space model and an ensemble Kalman filter and smoothing algorithm for on-line and retrospective state estimation. Our approach addresses the high dimensionality, measurement bias, and nonlinearities inherent in satellite data. We apply the method to a sequence of SeaWiFS satellite images in…

平面各向同性高斯随机场的形变估计AI 解读

This paper presents a new approach to the estimation of the deformation of an isotropic Gaussian random field on ℝ2 based on dense observations of a single realization of the deformed random field. Under this framework we investigate the identification and estimation of deformations. We then present a complete methodological package—from model assumptions to algorithmic recovery of the deformation—for the class of nonstationary processes obtained by deforming isotropic…

规则监测数据的统计方法AI 解读

Summary Meteorological and environmental data that are collected at regular time intervals on a fixed monitoring network can be usefully studied combining ideas from multiple time series and spatial statistics, particularly when there are little or no missing data. This work investigates methods for modelling such data and ways of approximating the associated likelihood functions. Models for processes on the sphere crossed with time are emphasized, especially models that are…

大型空间数据集的似然近似AI 解读

Summary Likelihood methods are often difficult to use with large, irregularly sited spatial data sets, owing to the computational burden. Even for Gaussian models, exact calculations of the likelihood for n observations require O(n3) operations. Since any joint density can be written as a product of conditional densities based on some ordering of the observations, one way to lessen the computations is to condition on only some of the ‘past’ observations when computing the…

空间数据插值：克里金法的若干理论AI 解读

1 Linear Prediction.- 1.1 Introduction.- 1.2 Best linear prediction.- Exercises.- 1.3 Hilbert spaces and prediction.- Exercises.- 1.4 An example of a poor BLP.- Exercises.- 1.5 Best linear unbiased prediction.- Exercises.- 1.6 Some recurring themes.- The Matern model.- BLPs and BLUPs.- Inference for differentiable random fields.- Nested models are not tenable.- 1.7 Summary of practical suggestions.- 2 Properties of Random Fields.- 2.1 Preliminaries.- Stationarity.-…

各向同性随机场的局部格点抽样设计AI 解读

For predicting $\int_G v(x)Z(x)dx$, where v is a fixed known function and Z is a stationary random field, a good sampling fesign should have a greater density of observations where v is relatively large in absolute value. Designs using this idea when $G = [0, 1]$ have been studied for some time. For G a region in two dimensions, very little is known about the statistical properties of cubature rules based on designs with varying density. This work proposes a class of designs…

利用格点观测预测随机场积分AI 解读

For a stationary random field Z on $\mathbb{R}^d$, this work studies the asymptotic behavior of predictors of $\int v(x)Z(x)dx$ based on observations on a lattice as the distance between neighbors in the lattice tends to 0. Under a mild condition on the spectral density of Z, an asymptotic expression for the mean-squared error of a predictor of $\int v(x)Z(x)dx$ based on observations on an infinite lattice is derived. For predicting integrals over the unit cube, a simple…

空间周期图的固定域渐近理论AI 解读

The periodogram for a spatial process observed on a lattice is often used to estimate the spectral density. The bases for such estimators are two asymptotic properties that periodograms commonly possess: (1) the periodogram at a particular frequency is approximately unbiased for the spectral density, and (2) the correlation of the periodogram at distinct frequencies is approximately zero. For spatial data, it is often appropriate to use fixed-domain asymptotics in which the…

阶数参数可估计的平滑样条AI 解读

Smoothing splines of a fixed order are commonly used as nonparametric regression estimates. The only parameter, then, that needs to be estimated is the smoothing parameter, which is often estimated using some form of cross validation. This work allows the order of the smoothing spline to be estimated using a model in which the order parameter is continuous. Within this setting, generalized cross validation and modified maximum likelihood estimates of the order and smoothing…

点过程约化二阶矩测度的渐近最优估计AI 解读

A modification of the rigid motion correction estimator for the reduced second moment measure of a stationary, isotropic point process is shown to possess an asymptotic optimality property as the observation region grows. Furthermore, a modification of the translation correction estimator for stationary, anisotropic point processes has a similar optimality property.

点过程约化二阶矩测度的渐近最优估计AI 解读

A modification of the rigid motion correction estimator for the reduced second moment measure of a stationary, isotropic point process is shown to possess an asymptotic optimality property as the observation region grows. Furthermore, a modification of the translation correction estimator for stationary, anisotropic point processes has a similar optimality property.

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点过程约化二阶矩测度的一类新估计量AI 解读

A class of edge-corrected estimators for the reduced second moment measure of a stationary isotropic point process is proposed. All estimators in this class have the same unbiasedness properties as previously suggested edge-corrected estimators. One natural element in this class has superior asymptotic properties to existing estimators when the underlying process is Poisson. A simulation study shows that this superiority also holds for certain cluster processes.

随机过程参数估计中广义交叉验证与修正最大似然的比较AI 解读

Wahba compared the performance of generalized cross validation (GCV) and modified maximum likelihood (MML) procedures for choosing the smoothing parameter of a smoothing spline. This work makes a more careful study of the two procedures when the stochastic model motivating the modified maximum likelihood estimate is correct. In particular, it is shown that in the case of the linear smoothing spline with equally spaced observations, both estimates are asymptotically normal…

协方差函数错设时线性预测效率的界AI 解读

Suppose $z(\cdot)$ is a random process defined on a bounded set $R \subset \mathbb{R}^1$ with finite second moments. Consider the behavior of linear predictions based on $z(t_1), \ldots, z(t_n)$, where $t_1, t_2, \cdots$ is a dense sequence of points in $R$. Stein showed that if the second-order structure used to generate the predictions is incorrect but compatible with the correct second-order structure, the obtained predictions are uniformly asymptotically optimal as $n \rightarrow \infty$…

协方差函数误设下随机场预测的渐近有效性AI 解读

Best linear unbiased predictors of a random field can be obtained if the covariance function of the random field is specified correctly. Consider a random field defined on a bounded region $R$. We wish to predict the random field $z(\cdot)$ at a point $x$ in $R$ based on observations $z(x_1), z(x_2), \ldots, z(x_N)$ in $R$, where $\{x_i\}^\infty_{i = 1}$ has $x$ as a limit point but does not contain $x$. Suppose the covariance function is misspecified, but has an equivalent…

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