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Nonparametric Empirical Bayes Prediction

非参数经验贝叶斯预测

Henceforth, the Journal of the American Statistical Association 2022 Theory and Methods Invited Paper by Nikolaos Ignatiadis and Stefan Wager, “Nonparametric Empirical Bayes Analysis,” will be refe...

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Multivariate spatial covariance models: a conditional approach

多元空间协方差模型的条件构造方法

Multivariate geostatistics is based on modelling all covariances between all possible combinations of two or more variables at any sets of locations in a continuously indexed domain. Multivariate spatial covariance models need to be built with care, since any covariance matrix that is derived from such a model must be nonnegative-definite. In this article, we develop a conditional approach for spatial-model construction whose validity conditions are easy to check. We start…

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Spatial Statistical Data Fusion for Remote Sensing Applications

面向遥感应用的空间统计数据融合

Aerosols are tiny solid or liquid particles suspended in the atmosphere; examples of aerosols include windblown dust, sea salts, volcanic ash, smoke from wildfires, and pollution from factories. The global distribution of aerosols is a topic of great interest in climate studies since aerosols can either cool or warm the atmosphere depending on their location, type, and interaction with clouds. Aerosol concentrations are important input components of global climate models,…

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Bayesian Inference for the Spatial Random Effects Model

空间随机效应模型的贝叶斯推断

Spatial statistical analysis of massive amounts of spatial data can be challenging because computation of optimal procedures can break down. The Spatial Random Effects (SRE) model uses a fixed number of known but not necessarily orthogonal (multiresolutional) spatial basis functions, which gives a flexible family of nonstationary covariance functions, results in dimension reduction, and yields optimal spatial predictors whose computations are scalable. By modeling spatial…

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Comment: Hierarchical Statistical Modeling for Paleoclimate Reconstruction

评论:古气候重建的层级统计建模

The article by Bo Li, Douglas W. Nychka, and Caspar M. Ammann (hereafter, LNA) has several goals. It considers the important problem of reconstruction of past (over a period of more than 1000 Years Before Present) climate from multiproxy data, and it directly recognizes the various uncertainties in this undertaking. These uncertainties are expressed through (conditional) probability distributions in a framework known to readers of this journal as hierarchical statistical…

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A Fast, Optimal Spatial-Prediction Method for Massive Datasets

面向海量数据的快速最优空间预测方法

This article considers a class of multiresolution tree-structured models that are spatially shifted versions of each other and proposes a new spatial-prediction method that averages over the optimal spatial predictors produced from members of this class of models. As a consequence, the resulting predicted surface is smooth, even when the predictors generated separately from individual multiresolution tree-structured models are not. We call the new predictor the…

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Nonparametric Hypothesis Testing for a Spatial Signal

空间信号的非参数假设检验

Nonparametric hypothesis testing for a spatial signal can involve a large number of hypotheses. For instance, two satellite images of the same scene, taken before and after an event, could be used to test a hypothesis that the event has no environmental impact. This is equivalent to testing that the mean difference of “after-before” is zero at each of the (typically thousands of) pixels that make up the scene. In such a situation, conventional testing procedures that control…

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Classes of Nonseparable, Spatio-Temporal Stationary Covariance Functions

非可分时空平稳协方差函数类

Suppose that a random process Z(s;t), indexed in space and time, has spatio-temporal stationary covariance C(h;u), where h ∈ ℝd (d ≥ 1) is a spatial lag and u ∈ ℝ is a temporal lag. Separable spatio-temporal covariances have the property that they can be written as a product of a purely spatial covariance and a purely temporal covariance. Their ease of definition is counterbalanced by the rather limited class of random processes to which they correspond. In this article we…

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Prediction of Spatial Cumulative Distribution Functions Using Subsampling

利用子抽样预测空间累积分布函数

The spatial cumulative distribution function (SCDF) is a random function that provides a statistical summary of a random field over a spatial domain of interest. In this article we develop a spatial subsampling method for predicting an SCDF based on observations made on a hexagonal grid, similar to the one used in the Environmental Monitoring and Assessment Program of the U.S. Environmental Protection Agency. We show that under quite general conditions, the proposed…

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An Approach to Statistical Spatial-Temporal Modeling of Meteorological Fields: Comment

气象场时空统计建模方法:评论

Abstract In this article we develop a random field model for the mean temperature over the region in the northern United States covering eastern Montana through the Dakotas and northern Nebraska up to the Canadian border. The readings are temperatures at the stations in the U.S. historical climatological network. The stochastic structure is modeled by a stationary spatial-temporal Gaussian random field. For this region, we find little evidence of temporal dependence while…

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The VPRT: A Sequential Testing Procedure Dominating the SPRT

VPRT:相较 SPRT 的可变样本量序贯检验程序

Under more general assumptions than those usually made in the sequential analysis literature, a variable-sample-size-sequential probability ratio test (VPRT) of two simple hypotheses is found that maximizes the expected net gain over all sequential decision procedures. In contrast, Wald and Wolfowitz [25] developed the sequential probability ratio test (SPRT) to minimize expected sample size, but their assumptions on the parameters of the decision problem were restrictive.…

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A Spatial Statistical Analysis of Tumor Growth

肿瘤生长的空间统计分析

Growth models for tumors commonly are developed for a one-dimensional summary (e.g., number of cells, volume). At the supracellular level, however, ignoring tumor shape leads to oversimplification of the growth mechanism. For example, an oncologist would view the discovery of a compact tumor of volume v differently from the discovery of two osculatory compact tumors each of volume v/2. This article presents a growth model that uses shape information at a previous time to…

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A Spatial Statistical Analysis of Tumor Growth

肿瘤生长的空间统计分析

Growth models for tumors commonly are developed for a one-dimensional summary (e.g., number of cells, volume). At the supracellular level, however, ignoring tumor shape leads to oversimplification of the growth mechanism. For example, an oncologist would view the discovery of a compact tumor of volume v differently from the discovery of two osculatory compact tumors each of volume v/2. This article presents a growth model that uses shape information at a previous time to…

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Empirical Bayes Estimation of Undercount in the Decennial Census

十年人口普查漏计的经验贝叶斯估计

On April 1, 1990, the decennial census for the United States will be conducted by the U.S. Bureau of the Census. By December 31, 1990, the Census Bureau is specified by law to submit state population counts for the purpose of reapportionment of the U.S. House of Representatives, and by March 31, 1991, to submit small-area population counts for the purpose of redistricting. Census counts are used in a variety of other ways: for revenue-sharing formulas between different…

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A Graphical Procedure for Determining Nonstationarity in Time Series

判定时间序列非平稳性的图形方法

Integrated processes as models for time series data have proved to be an important component of the highly flexible class of ARIMA(p, d, q) models. Determining the amount of differencing, d, has been a difficult task: too little and the process is not yet second-order stationary; too much and the process is more variable than it need be. It is shown that by introducing the notion of generalized covariances, developed by Matheron (1973) for spatial processes, the amount of…

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Kriging Nonstationary Data

非平稳空间数据的克里金预测

Spatial data modeled to have come from a random function with a nonstationary mean are considered. The spatial prediction method known as kriging exploits second-order spatial correlation structure to obtain minimum variance predictions of certain average values of the random function. But to do so, it must be assumed that either the mean function (the drift) is known up to a constant or the second-order structure (the variogram) is known exactly. Knowledge of the drift…

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Empirical Bayes Estimation in Sampling Inspection

抽样检验中的经验贝叶斯估计

This paper considers an empirical Bayes approach to estimating the number of defectives in a hypergeometric distribution. It involves application of an empirical Bayes identity which has proved useful in providing simple Bayes estimates for various other models. Data on the reliability of coding the answers to questions from a household survey illustrate how an empirical Bayes estimate can be computed without knowledge of the prior distribution of the number of defectives.

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Empirical Bayes estimation in sampling inspection

抽样检验中的经验贝叶斯估计

This paper considers an empirical Bayes approach to estimating the number of defectives in a hypergeometric distribution. It involves application of an empirical Bayes identity which has proved useful in providing simple Bayes estimates for various other models. Data on the reliability of coding the answers to questions from a household survey illustrate how an empirical Bayes estimate can be computed without knowledge of the prior distribution of the number of defectives.

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Multinomial Goodness-Of-Fit Tests

多项分布的拟合优度检验

SUMMARY This article investigates the family {I λ;λ ϵ ℝ} of power divergence statistics for testing the fit of observed frequencies {Xi; i = 1, …, k} to expected frequencies {Ei; i = 1, …, k}. From the definition 2nIλ=2λ(λ+1)∑i=1kXi{(XiEi)λ−1};λ∈ℝ it can easily be seen that Pearson's X 2 (λ = 1), the log likelihood ratio statistic (λ = 0), the Freeman-Tukey statistic (λ = –½) the modified log likelihood ratio statistic (λ = –1) and the Neyman modified X 2 (λ = –2), are all…

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A Useful Empirical Bayes Identity

一个有用的经验贝叶斯恒等式

For any decision problem, one wishes to find that estimator which minimizes the expected loss. If the loss function is squared error, then the estimator is the mean of the Bayes posterior distribution. Unfortunately the prior distribution may be unknown, but in certain situations empirical Bayes methods can circumvent this problem by using past observations to estimate either the prior or the Bayes estimate directly. Empirical Bayes methods are particularly appealing when…

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Transformations and the Jackknife

变换与刀切法

SUMMARY Whether to jackknife a parameter estimate or some function of it has in the past relied on recommendations based on folklore rather than statistical analyses. This paper puts the jackknifed estimator into the context of finding a least squares estimator from a model involving a location parameter with additive “pseudo” errors. Obtaining and interpreting confidence intervals from the partitioning of the “total” sum of squares into “residual” sum of squares, plus a sum…

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An Optimal Statistic Based on Higher Order Gaps

基于高阶间距的最优统计量

The family of statistics based on mth-order gaps from a uniform sample, obtained by summing a suitably regular function of each gap, is investigated. Holst (1979) has established asymptotic normality together with explicit expressions for the mean and variance. This is extended to samples from distributions whose perturbations from uniformity HhrinV as sample size grows. From these results, Pitman asymptotic relative efficiencies can be calculated, and it is shown that the…

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An optimal statistic based on higher order gaps

基于高阶间距的最优统计量

The family of statistics based on mth-order gaps from a uniform sample, obtained by summing a suitably regular function of each gap, is investigated. Holst (1979) has established asymptotic normality together with explicit expressions for the mean and variance. This is extended to samples from distributions whose perturbations from uniformity HhrinV as sample size grows. From these results, Pitman asymptotic relative efficiencies can be calculated, and it is shown that the…

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