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  • Introducing covariate dependent weighting matrices in fitting autoregressive models and measuring spatio-environmental autocorrelation
    Spat. Stat. (IF 1.219) Pub Date : 2020-05-29
    Bedilu Alamirie Ejigu; Eshetu Wencheko

    In lattice type of spatial data analysis, the choice of spatial weighting matrices is a main component of any spatial autocorrelation measures and spatial autoregressive models because the choice assumes priori structures of spatial dependency. Typically, weighting matrices are constructed based on the concept of spatial proximity or geographical distance-based functions. However, depending on the

    更新日期:2020-05-29
  • Use of fractals to measure anisotropy in point patterns extracted with the DPT of an image
    Spat. Stat. (IF 1.219) Pub Date : 2020-05-26
    I. Fabris-Rotelli; A. Stein

    Images are particular and well–known instances of spatial big data. Typically spatial data are scale specific and in this paper, we propose mechanisms to effectively address issues of scale in the analysis of images. We focus on spatial data extracted from images using the Discrete Pulse Transform (DPT). The DPT extracts discrete pulses from images at multiple scales that are recognisable as connected

    更新日期:2020-05-26
  • Families of covariance functions for bivariate random fields on spheres
    Spat. Stat. (IF 1.219) Pub Date : 2020-05-22
    Moreno Bevilacqua; Peter Diggle; Emilio Porcu

    This paper proposes a new class of covariance functions for bivariate random fields on spheres, having the same properties as the bivariate Matérn model proposed in Euclidean spaces. The new class depends on the geodesic distance on a sphere; it allows for indexing differentiability (in the mean square sense) and fractal dimensions of the components of any bivariate Gaussian random field having such

    更新日期:2020-05-22
  • On the measurement of bias in geographically weighted regression models
    Spat. Stat. (IF 1.219) Pub Date : 2020-05-22
    Hanchen Yu; A. Stewart Fotheringham; Ziqi Li; Taylor Oshan; Levi John Wolf

    Under the realization that Geographically Weighted Regression (GWR) is a data-borrowing technique, this paper derives expressions for the amount of bias introduced to local parameter estimates by borrowing data from locations where the processes might be different from those at the regression location. This is done for both GWR and Multiscale GWR (MGWR). We demonstrate the accuracy of our expressions

    更新日期:2020-05-22
  • Projections of determinantal point processes
    Spat. Stat. (IF 1.219) Pub Date : 2020-05-21
    Adrien Mazoyer; Jean-François Coeurjolly; Pierre-Olivier Amblard

    Let x={x(1),…,x(n)} be a space filling-design of n points defined in 0,1d. In computer experiments, an important property seek for x is a nice coverage of 0,1d. This property could be desirable as well as for any projection of x onto 0,1ι for ι

    更新日期:2020-05-21
  • Collective spectral density estimation and clustering for spatially-correlated data
    Spat. Stat. (IF 1.219) Pub Date : 2020-05-16
    Tianbo Chen; Ying Sun; Mehdi Maadooliat

    In this paper, we develop a method for estimating and clustering two-dimensional spectral density functions (2D-SDFs) for spatial data from multiple subregions. We use a common set of adaptive basis functions to explain the similarities among the 2D-SDFs in a low-dimensional space and estimate the basis coefficients by maximizing the Whittle likelihood with two penalties. We apply these penalties to

    更新日期:2020-05-16
  • Bayesian estimation of spatial filters with Moran’s eigenvectors and hierarchical shrinkage priors
    Spat. Stat. (IF 1.219) Pub Date : 2020-05-11
    Connor Donegan; Yongwan Chun; Amy E. Hughes

    This paper proposes a Bayesian method for spatial regression using eigenvector spatial filtering (ESF) and Piironen and Vehtari (2017)’s regularized horseshoe (RHS) prior. ESF models are most often estimated using variable selection procedures such as stepwise selection, but in the absence of a Bayesian model averaging procedure variable selection methods cannot properly account for parameter uncertainty

    更新日期:2020-05-11
  • Deformed SPDE models with an application to spatial modeling of significant wave height
    Spat. Stat. (IF 1.219) Pub Date : 2020-05-07
    Anders Hildeman; David Bolin; Igor Rychlik

    A non-stationary Gaussian random field model is developed based on a combination of the stochastic partial differential equation (SPDE) approach and the classical deformation method. With the deformation method, a stationary field is defined on a domain which is deformed so that the field becomes non-stationary. We show that if the stationary field is a Matérn field defined as a solution to a fractional

    更新日期:2020-05-07
  • Time varying complex covariance functions for oceanographic data
    Spat. Stat. (IF 1.219) Pub Date : 2020-04-28
    C. Cappello; S. De Iaco; S. Maggio; D. Posa

    In Geostatistics, vector data with two components, such as measures for wind field, electromagnetic field and ocean currents can be appropriately modeled by recalling the theory of complex-valued random fields. This is especially suitable for describing phenomena whose variables are expressed in the same unit of measurement and refer to homogeneous quantities. In this paper, a first approach useful

    更新日期:2020-04-28
  • Accounting for spatial varying sampling effort due to accessibility in Citizen Science data: A case study of moose in Norway
    Spat. Stat. (IF 1.219) Pub Date : 2020-04-18
    Jorge Sicacha-Parada; Ingelin Steinsland; Benjamin Cretois; Jan Borgelt

    Citizen Scientists together with an increasing access to technology provide large datasets that can be used to study e.g. ecology and biodiversity. Unknown and varying sampling effort is a major issue when making inference based on citizen science data. In this paper we propose a modeling approach for accounting for variation in sampling effort due to accessibility. The paper is based on an illustrative

    更新日期:2020-04-24
  • Prediction of intensity and location of seismic events using deep learning
    Spat. Stat. (IF 1.219) Pub Date : 2020-04-14
    Orietta Nicolis; Francisco Plaza

    The object of this work is to predict the seismic rate in Chile by using two Deep Neural Network (DNN) architectures, Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). For this, we propose a methodology based on a three-module approach: a pre-processing module, a spatial and temporal estimation module, and a prediction module. The first module considers the Epidemic-Type Aftershock

    更新日期:2020-04-14
  • Heteroskedastic geographically weighted regression model for functional data
    Spat. Stat. (IF 1.219) Pub Date : 2020-04-13
    E. Romano; J. Mateu; O. Butzbach

    A large number of approaches for modelling spatially dependent functional variables often assume that the functional regression coefficients are constant over the region of interest. However, in many occasions it is far more realistic that functional coefficients vary at a local level. The present paper proposes a calibrated heteroskedastic geographically weighted regression model (H-GWR) in the functional

    更新日期:2020-04-13
  • Semi-parametric resampling with extremes
    Spat. Stat. (IF 1.219) Pub Date : 2020-04-13
    Thomas Opitz; Denis Allard; Grégoire Mariethoz

    Nonparametric resampling methods such as Direct Sampling are powerful tools to simulate new datasets preserving important data features such as spatial patterns from observed datasets while using only minimal assumptions. However, such methods cannot generate extreme events beyond the observed range of data values. We here propose using tools from extreme value theory for stochastic processes to extrapolate

    更新日期:2020-04-13
  • Analyzing car thefts and recoveries with connections to modeling origin–destination point patterns
    Spat. Stat. (IF 1.219) Pub Date : 2020-04-09
    Shinichiro Shirota; Alan. E. Gelfand; Jorge Mateu

    For a given region, we have a dataset composed of car theft locations along with a linked dataset of recovery locations which, due to partial recovery, is a relatively small subset of the set of theft locations. For an investigator seeking to understand the behavior of car thefts and recoveries in the region, several questions are addressed. Viewing the set of theft locations as a point pattern, can

    更新日期:2020-04-09
  • Determining the spatial effects of COVID-19 using the spatial panel data model
    Spat. Stat. (IF 1.219) Pub Date : 2020-04-07
    Hasraddin Guliyev

    This study investigates the propagation power and effects of the coronavirus disease 2019 (COVID-19) in light of published data. We examine the factors affecting COVID-19 together with the spatial effects, and use spatial panel data models to determine the relationship among the variables including their spatial effects. Using spatial panel models, we analyse the relationship between confirmed cases

    更新日期:2020-04-07
  • Sequential process to choose efficient sampling design based on partial prior information data and simulations
    Spat. Stat. (IF 1.219) Pub Date : 2020-04-06
    Claire Kermorvant; Sébastien Coube; Frank D’amico; Noëlle Bru; Nathalie Caill-Milly

    Issues on sampling procedure definition led numerous study results to be biased and object of controversy. Choosing relevant sampling design and number of samples is a difficult task when wanted to set up or optimize a survey. The survey design choice is very important to avoid bias and increase the survey cost-efficiency. It can have a strong effect on the sample size needed to achieve some targeted

    更新日期:2020-04-06
  • Identifying spatial patterns with the Bootstrap ClustGeo technique
    Spat. Stat. (IF 1.219) Pub Date : 2020-04-03
    Veronica Distefano; Valentina Mameli; Irene Poli

    Building clusters for pattern recognition and analysis of geographical areas can be a useful way to provide relevant information for economic and social decisions. In this paper, we introduce a novel spatial clustering technique, called Bootstrap ClustGeo (BCG), which is a hierarchical approach, based on bootstrap techniques with spatial constraints. We evaluate the performance of the proposed approach

    更新日期:2020-04-03
  • Developments in statistical inference when assessing spatiotemporal disease clustering with the tau statistic
    Spat. Stat. (IF 1.219) Pub Date : 2020-03-23
    Timothy M. Pollington; Michael J. Tildesley; T. Déirdre Hollingsworth; Lloyd A.C. Chapman
    更新日期:2020-03-23
  • Nonparametric spatiotemporal analysis of violent crime. A case study in the Rio de Janeiro metropolitan area
    Spat. Stat. (IF 1.219) Pub Date : 2020-03-13
    I. Fuentes-Santos; W. González-Manteiga; J.P. Zubelli

    This paper analyzes the spatiotemporal pattern of gunfire reports collected by the collaborative mobile app Fogo Cruzado in the Rio de Janeiro metropolitan area (Brazil). We apply nonparametric first and second-order inference tools to characterize gunfire behavior, and test whether gunfire patterns meet the assumptions of crime prediction models, such as kernel hotspot maps or self-exciting point

    更新日期:2020-03-13
  • Mapping occupational health risk factors in the primary sector—A novel supervised machine learning and Area-to-Point Poisson kriging approach
    Spat. Stat. (IF 1.219) Pub Date : 2020-03-07
    S. Gerassis; C. Boente; M.T.D. Albuquerque; M.M. Ribeiro; A. Abad; J. Taboada

    Workers around the world spend nearly a quarter of their time at work Occupational health is gaining great importance due to the profound impact on people long term health. The health status of the primary sector workforce is a great unknown for medical geography where health maps and spatial patterns have not been able to explain years of changing disease rates. This article proposes a new approach

    更新日期:2020-03-07
  • Analysing point patterns on networks — A review
    Spat. Stat. (IF 1.219) Pub Date : 2020-03-05
    Adrian Baddeley; Gopalan Nair; Suman Rakshit; Greg McSwiggan; Tilman M. Davies

    We review recent research on statistical methods for analysing spatial patterns of points on a network of lines, such as road accident locations along a road network. Due to geometrical complexities, the analysis of such data is extremely challenging, and we describe several common methodological errors. The intrinsic lack of homogeneity in a network militates against the traditional methods of spatial

    更新日期:2020-03-05
  • Testing global and local dependence of point patterns on covariates in parametric models
    Spat. Stat. (IF 1.219) Pub Date : 2020-03-03
    Mari Myllymäki; Mikko Kuronen; Tomáš Mrkvička

    Testing for a covariate effect in a parametric point process model is usually done through the Wald test, which relies on an asymptotic null distribution of the test statistic. We propose a Monte Carlo version of the test that also allows local investigation of the covariate effect in the globally fitted model. Two different test statistics are suggested for this purpose: the first, a spatial statistic

    更新日期:2020-03-03
  • Point-process based Bayesian modeling of space–time structures of forest fire occurrences in Mediterranean France
    Spat. Stat. (IF 1.219) Pub Date : 2020-02-29
    Thomas Opitz; Florent Bonneu; Edith Gabriel

    Due to climate change and human activity, wildfires are expected to become more frequent and extreme worldwide, causing economic and ecological disasters. The deployment of preventive measures and operational forecasts can be aided by stochastic modeling that helps to understand and quantify the mechanisms governing the occurrence intensity. We here develop a point process framework for wildfire ignition

    更新日期:2020-02-29
  • Understanding the effects of dichotomization of continuous outcomes on geostatistical inference
    Spat. Stat. (IF 1.219) Pub Date : 2020-02-28
    Irene Kyomuhangi; Tarekegn A. Abeku; Matthew J. Kirby; Gezahegn Tesfaye; Emanuele Giorgi

    Diagnosis is often based on the exceedance or not of continuous health indicators of a predefined cut-off value, so as to classify patients into positives and negatives for the disease under investigation. In this paper, we investigate the effects of dichotomization of spatially-referenced continuous outcome variables on geostatistical inference. Although this issue has been extensively studied in

    更新日期:2020-02-28
  • Spatial modeling of repeated events with an application to disease mapping
    Spat. Stat. (IF 1.219) Pub Date : 2020-02-28
    Shabnam Balamchi; Mahmoud Torabi

    Mixed models are commonly used to analyze spatial data which frequently occur in practice such as in health sciences and life studies. It is customary to incorporate spatial random effects into the model to account for spatial variation of the data. In particular, Poisson mixed models are used to analyze spatial count data. It is often assumed that the observations in each area, conditional on the

    更新日期:2020-02-28
  • Revisiting the random shift approach for testing in spatial statistics
    Spat. Stat. (IF 1.219) Pub Date : 2020-02-25
    Tomáš Mrkvička; Jiří Dvořák; Jonatan A. González; Jorge Mateu

    We consider the problem of non-parametric testing of independence of two components of a stationary bivariate spatial process. In particular, we revisit the random shift approach that has become the standard method for testing the independent superposition hypothesis in spatial statistics, and it is widely used in a plethora of practical applications. However, this method has a problem of liberality

    更新日期:2020-02-25
  • Statistical challenges in spatial analysis of plant ecology data
    Spat. Stat. (IF 1.219) Pub Date : 2020-02-24
    Alan E. Gelfand

    The scope of spatial statistics problems arising in ecology is substantial. They concern both plant and animal behavior. Here, we discuss only plants. The range of issues is still very large and so we confine ourselves to four challenges, each within the context of model development. These are: species distribution models, both individual and joint; preferential sampling and bias in presence/absence

    更新日期:2020-02-24
  • Automating a Process Convolution Approach to Account for Spatial Information in Imaging Mass Spectrometry Data.
    Spat. Stat. (IF 1.219) Pub Date : 2020-02-19
    Cameron Miller,Andrew Lawson,Dongjun Chung,Mulugeta Gebregziabher,Elizabeth Yeh,Richard Drake,Elizabeth Hill

    In the age of big data, imaging techniques such as imaging mass spectrometry (IMS) stand out due to the combination of data size and spatial referencing. However, the data analytic tools readily accessible to investigators often ignore the spatial information or provide results with vague interpretations. We focus on imaging techniques like IMS that collect data along a regular grid and develop methods

    更新日期:2020-02-19
  • Modeling massive spatial datasets using a conjugate Bayesian linear modeling framework
    Spat. Stat. (IF 1.219) Pub Date : 2020-02-07
    Sudipto Banerjee

    Geographic Information Systems (GIS) and related technologies have generated substantial interest among statisticians with regard to scalable methodologies for analyzing large spatial datasets. A variety of scalable spatial process models have been proposed that can be easily embedded within a hierarchical modeling framework to carry out Bayesian inference. While the focus of statistical research has

    更新日期:2020-02-07
  • Great expectations and even greater exceedances from spatially referenced data
    Spat. Stat. (IF 1.219) Pub Date : 2020-02-07
    Noel Cressie; Thomas Suesse

    We clear land for agricultural purposes, we draw water from streams and aquifers, and we build houses in coastal regions for their ready access to the sea. Our need for food, water, and shelter is basic, but so is variability in our natural environment. Understanding this is key to the long-term sustainability of the anthropocene. At any location in the environment, long-term temporal averages may

    更新日期:2020-02-07
  • Imputed spatial data: Cautions arising from response and covariate imputation measurement error
    Spat. Stat. (IF 1.219) Pub Date : 2020-02-03
    Daniel A. Griffith; Yan-Ting Liau

    When data for observations are missing, scientists often remove those observations from an analysis, or replace them with imputations, with few other options available to these analysts. Confining an analysis to those observations with complete data can waste resources expended for, especially, those observations with near-complete data. Selectively retaining variables with complete data for observations

    更新日期:2020-02-03
  • Flexible spatial covariance functions
    Spat. Stat. (IF 1.219) Pub Date : 2020-01-30
    Alexandra M. Schmidt; Peter Guttorp

    We focus on the discussion of modeling processes that are observed at fixed locations of a region (geostatistics). A standard approach is to assume that the process of interest follows a Gaussian Process with some mean and (valid) covariance functions. It is common to model the covariance function as the product between a variance parameter, and a correlation function which is a function of the Euclidean

    更新日期:2020-01-30
  • A sandwich smoother for spatio-temporal functional data
    Spat. Stat. (IF 1.219) Pub Date : 2020-01-28
    Joshua P. French; Piotr S. Kokoszka

    Statistical analysis of spatio-temporal data has been evolving to handle increasingly large data sets. For example, the North American CORDEX program is producing daily values of climate-related variables on spatial grids with approximately 100,000 locations over 150 years. Smoothing of such massive and noisy data is essential to understanding their spatio-temporal features. It also reduces the size

    更新日期:2020-01-28
  • Approximately optimal spatial design: How good is it?
    Spat. Stat. (IF 1.219) Pub Date : 2020-01-28
    Yu Wang; Nhu D. Le; James V. Zidek

    The increasing recognition of the association between adverse human health conditions and many environmental substances as well as processes has led to the need to monitor them. An important problem that arises in environmental statistics is the design of the locations of the monitoring stations for those environmental processes of interest. One particular design criterion for monitoring networks that

    更新日期:2020-01-28
  • Nonstationary cross-covariance functions for multivariate spatio-temporal random fields
    Spat. Stat. (IF 1.219) Pub Date : 2020-01-25
    Mary Lai O. Salvaña; Marc G. Genton

    In multivariate spatio-temporal analysis, we are faced with the formidable challenge of specifying a valid spatio-temporal cross-covariance function, either directly or through the construction of processes. This task is difficult as these functions should yield positive definite covariance matrices. In recent years, we have seen a flourishing of methods and theories on constructing spatio-temporal

    更新日期:2020-01-25
  • Extra-parametrized extreme value copula : Extension to a spatial framework
    Spat. Stat. (IF 1.219) Pub Date : 2020-01-24
    J. Carreau; G. Toulemonde

    Hazard assessment at a regional scale may be performed thanks to a spatial model for maxima that can be obtained by combining the generalized extreme-value (GEV) distribution for the univariate marginal distributions with extreme-value copulas to describe their dependence structure, as justified by the theory of multivariate extreme values. A flexible class of extreme-value copulas, called XGumbel

    更新日期:2020-01-24
  • Deep integro-difference equation models for spatio-temporal forecasting
    Spat. Stat. (IF 1.219) Pub Date : 2020-01-16
    Andrew Zammit-Mangion; Christopher K. Wikle

    Integro-difference equation (IDE) models describe the conditional dependence between the spatial process at a future time point and the process at the present time point through an integral operator. Nonlinearity or temporal dependence in the dynamics is often captured by allowing the operator parameters to vary temporally, or by re-fitting a model with a temporally-invariant linear operator in a sliding

    更新日期:2020-01-16
  • Animal movement models with mechanistic selection functions
    Spat. Stat. (IF 1.219) Pub Date : 2019-12-31
    Mevin B. Hooten; Xinyi Lu; Martha J. Garlick; James A. Powell

    A suite of statistical methods are used to study animal movement. Most of these methods treat animal telemetry data in one of three ways: as discrete processes, as continuous processes, or as point processes. We briefly review each of these approaches and then focus in on the latter. In the context of point processes, so-called resource selection analyses are among the most common way to statistically

    更新日期:2019-12-31
  • Stochastic local interaction model with sparse precision matrix for space–time interpolation
    Spat. Stat. (IF 1.219) Pub Date : 2019-12-31
    Dionissios T. Hristopulos; Vasiliki D. Agou

    The application of geostatistical and machine learning methods based on Gaussian processes to big space–time data is beset by the requirement for storing and numerically inverting large and dense covariance matrices. Computationally efficient representations of space–time correlations can be constructed using local models of conditional dependence which can reduce the computational load. We formulate

    更新日期:2019-12-31
  • A spatial concordance correlation coefficient with an application to image analysis
    Spat. Stat. (IF 1.219) Pub Date : 2019-12-30
    Ronny Vallejos; Javier Pérez; Aaron M. Ellison; Andrew D. Richardson

    In this work we define a spatial concordance coefficient for second-order stationary processes. This problem has been widely addressed in a non-spatial context, but here we consider a coefficient that for a fixed spatial lag allows one to compare two spatial sequences along a 45°line. The proposed coefficient was explored for the bivariate Matérn and Wendland covariance functions. The asymptotic normality

    更新日期:2019-12-30
  • Problem-driven spatio-temporal analysis and implications for postgraduate statistics teaching
    Spat. Stat. (IF 1.219) Pub Date : 2019-12-28
    Peter J. Diggle

    The paper uses two case-studies, one in public health surveillance the other in veterinary epidemiology, to argue that the analysis strategy for spatio-temporal point process data should be guided by the scientific context in which the data were generated and, more particularly, by the objectives of the data analysis. This point of view is not specific to the point process setting and, in the author’s

    更新日期:2019-12-28
  • Spatio-temporal point patterns on linear networks: Pseudo-separable intensity estimation
    Spat. Stat. (IF 1.219) Pub Date : 2019-12-20
    Jorge Mateu; Mehdi Moradi; Ottmar Cronie

    Aside from reviewing different intensity estimation schemes for point processes on linear networks, this paper introduces two Voronoi-based intensity estimation approaches for spatio-temporal linear network point processes. The first is a separable estimator, which is obtained as a scaled product of a resample-smoothed Voronoi intensity estimator on the linear network in question and another one on

    更新日期:2019-12-20
  • Spatial mapping with Gaussian processes and nonstationary Fourier features.
    Spat. Stat. Pub Date : 2019-04-23
    Jean-Francois Ton,Seth Flaxman,Dino Sejdinovic,Samir Bhatt

    The use of covariance kernels is ubiquitous in the field of spatial statistics. Kernels allow data to be mapped into high-dimensional feature spaces and can thus extend simple linear additive methods to nonlinear methods with higher order interactions. However, until recently, there has been a strong reliance on a limited class of stationary kernels such as the Matérn or squared exponential, limiting

    更新日期:2019-11-01
  • A spatially varying change points model for monitoring glaucoma progression using visual field data.
    Spat. Stat. Pub Date : 2019-04-02
    Samuel I Berchuck,Jean-Claude Mwanza,Joshua L Warren

    Glaucoma disease progression, as measured by visual field (VF) data, is often defined by periods of relative stability followed by an abrupt decrease in visual ability at some point in time. Determining the transition point of the disease trajectory to a more severe state is important clinically for disease management and for avoiding irreversible vision loss. Based on this, we present a unified statistical

    更新日期:2019-11-01
  • Using a spatial point process framework to characterize lung computed tomography scans.
    Spat. Stat. (IF 1.219) Pub Date : 2018-12-31
    Brian E Vestal,Nichole E Carlson,Raúl San José Estépar,Tasha Fingerlin,Debashis Ghosh,Katerina Kechris,David Lynch

    Pulmonary emphysema is a destructive disease of the lungs that is currently diagnosed via visual assessment of lung Computed Tomography (CT) scans by a radiologist. Visual assessment can have poor inter-rater reliability, is time consuming, and requires access to trained assessors. Quantitative methods that reliably summarize the biologically relevant characteristics of an image are needed to improve

    更新日期:2019-11-01
  • Social Network Spatial Model.
    Spat. Stat. Pub Date : 2018-11-20
    Joseph T Ciminelli,Tanzy Love,Tong Tong Wu

    Our work is motivated by a desire to incorporate the vast wealth of social network data into the framework of spatial models. We introduce a method for modeling the spatial correlations that exist over a social network. In particular, we model attributes measured for each member of the network as a continuous process over the social space created by their connections. Our method simultaneously models

    更新日期:2019-11-01
  • Geostatistical estimation and prediction for censored responses.
    Spat. Stat. Pub Date : 2018-03-27
    José A Ordoñez,Dipankar Bandyopadhyay,Victor H Lachos,Celso R B Cabral

    Spatially-referenced geostatistical responses that are collected in environmental sciences research are often subject to detection limits, where the measures are not fully quantifiable. This leads to censoring (left, right, interval, etc), and various ad hoc statistical methods (such as choosing arbitrary detection limits, or data augmentation) are routinely employed during subsequent statistical analysis

    更新日期:2019-11-01
  • Is a matrix exponential specification suitable for the modeling of spatial correlation structures?
    Spat. Stat. Pub Date : 2018-03-02
    Magdalena E Strauß,Maura Mezzetti,Samantha Leorato

    This paper investigates the adequacy of the matrix exponential spatial specifications (MESS) as an alternative to the widely used spatial autoregressive models (SAR). To provide as complete a picture as possible, we extend the analysis to all the main spatial models governed by matrix exponentials comparing them with their spatial autoregressive counterparts. We propose a new implementation of Bayesian

    更新日期:2019-11-01
  • Model-based inference for small area estimation with sampling weights.
    Spat. Stat. Pub Date : 2017-10-11
    Y Vandendijck,C Faes,R S Kirby,A Lawson,N Hens

    Obtaining reliable estimates about health outcomes for areas or domains where only few to no samples are available is the goal of small area estimation (SAE). Often, we rely on health surveys to obtain information about health outcomes. Such surveys are often characterised by a complex design, stratification, and unequal sampling weights as common features. Hierarchical Bayesian models are well recognised

    更新日期:2019-11-01
  • Impact of Age, Race and Socio-economic Status on Temporal Trends in Late-Stage Prostate Cancer Diagnosis in Florida.
    Spat. Stat. Pub Date : 2015-12-09
    Pierre Goovaerts,Hong Xiao,Clement K Gwede,Fei Tan,Youjie Huang,Georges Adunlin,Askal Ali

    Individual-level data from the Florida Cancer Data System (1981-2007) were analysed to explore temporal trends of prostate cancer late-stage diagnosis, and how they vary based on race, income and age. Annual census-tract rates were computed for two races (white and black) and two age categories (40-65, >65) before being aggregated according to census tract median household incomes. Joinpoint regression

    更新日期:2019-11-01
  • Measuring Aggregation of Events about a Mass Using Spatial Point Pattern Methods.
    Spat. Stat. Pub Date : 2015-08-01
    Michael O Smith,Jackson Ball,Benjamin B Holloway,Ferenc Erdelyi,Gabor Szabo,Emily Stone,Jonathan Graham,J Josh Lawrence

    We present a methodology that detects event aggregation about a mass surface using 3-dimensional study regions with a point pattern and a mass present. The Aggregation about a Mass function determines aggregation, randomness, or repulsion of events with respect to the mass surface. Our method closely resembles Ripley's K function but is modified to discern the pattern about the mass surface. We briefly

    更新日期:2019-11-01
  • Exploration of the use of Bayesian modeling of gradients for censored spatiotemporal data from the Deepwater Horizon oil spill.
    Spat. Stat. Pub Date : 2015-01-20
    Harrison Quick,Caroline Groth,Sudipto Banerjee,Bradley P Carlin,Mark R Stenzel,Patricia A Stewart,Dale P Sandler,Lawrence S Engel,Richard K Kwok

    This paper develops a hierarchical framework for identifying spatiotemporal patterns in data with a high degree of censoring using the gradient process. To do this, we impute censored values using a sampling-based inverse CDF method within our Markov chain Monte Carlo algorithm, thereby avoiding burdensome integration and facilitating efficient estimation of other model parameters. We illustrate use

    更新日期:2019-11-01
  • A comparison of spatial smoothing methods for small area estimation with sampling weights.
    Spat. Stat. Pub Date : 2014-06-25
    Laina Mercer,Jon Wakefield,Cici Chen,Thomas Lumley

    Small area estimation (SAE) is an important endeavor in many fields and is used for resource allocation by both public health and government organizations. Often, complex surveys are carried out within areas, in which case it is common for the data to consist only of the response of interest and an associated sampling weight, reflecting the design. While it is appealing to use spatial smoothing models

    更新日期:2019-11-01
  • Spatio-temporal modeling for real-time ozone forecasting.
    Spat. Stat. Pub Date : 2013-09-07
    Lucia Paci,Alan E Gelfand,David M Holland

    The accurate assessment of exposure to ambient ozone concentrations is important for informing the public and pollution monitoring agencies about ozone levels that may lead to adverse health effects. High-resolution air quality information can offer significant health benefits by leading to improved environmental decisions. A practical challenge facing the U.S. Environmental Protection Agency (USEPA)

    更新日期:2019-11-01
  • Kernel Averaged Predictors for Spatio-Temporal Regression Models.
    Spat. Stat. Pub Date : 2013-09-07
    Matthew J Heaton,Alan E Gelfand

    In applications where covariates and responses are observed across space and time, a common goal is to quantify the effect of a change in the covariates on the response while adequately accounting for the spatio-temporal structure of the observations. The most common approach for building such a model is to confine the relationship between a covariate and response variable to a single spatio-temporal

    更新日期:2019-11-01
  • Hierarchical Modeling for Spatial Data Problems.
    Spat. Stat. Pub Date : 2012-05-01
    Alan E Gelfand

    This short paper is centered on hierarchical modeling for problems in spatial and spatio-temporal statistics. It draws its motivation from the interdisciplinary research work of the author in terms of applications in the environmental sciences - ecological processes, environmental exposure, and weather modeling. The paper briefly reviews hierarchical modeling specification, adopting a Bayesian perspective

    更新日期:2019-11-01
  • Nonparametric bootstrap approach for unconditional risk mapping under heteroscedasticity
    Spat. Stat. (IF 1.219) Pub Date : 2019-10-08
    Sergio Castillo-Páez; Rubén Fernández-Casal; Pilar García-Soidán

    The current work provides a nonparametric resampling procedure for approximating the (unconditional) probability that a spatial variable surpasses a prefixed threshold value. The existing approaches for the latter issue require assuming constant variance throughout the observation region, thus our proposal has been designed to be valid under heteroscedasticity of the spatial process. To develop the

    更新日期:2019-10-08
  • Non-stationary spatial regression for modelling monthly precipitation in Germany
    Spat. Stat. (IF 1.219) Pub Date : 2019-09-27
    Isa Marques; Nadja Klein; Thomas Kneib

    It is widely accepted that spatial dependencies have to be acknowledged appropriately in data that are spatially aligned. However, most spatial models still assume that the dependence structure does not vary over space, i.e., it is stationary. While assuming stationarity considerably facilitates estimation, it is often too restrictive when describing atmospheric phenomena such as precipitation. Nonetheless

    更新日期:2019-09-27
  • Some links between conditional and coregionalized multivariate Gaussian Markov random fields
    Spat. Stat. (IF 1.219) Pub Date : 2019-08-23
    Miguel A. Martinez-Beneito

    Multivariate disease mapping models are attracting considerable attention. Many modeling proposals have been made in this area, which could be grouped into three large sets: coregionalization, multivariate conditional and univariate conditional models. In this work we establish some links between these three groups of proposals. Specifically, we explore the equivalence between the two conditional approaches

    更新日期:2019-08-23
  • Testing for significant differences between two spatial patterns using covariates
    Spat. Stat. (IF 1.219) Pub Date : 2019-07-08
    M.I. Borrajo; W. González-Manteiga; M.D. Martínez-Miranda

    This paper addresses the problem of comparing the spatial distribution of two point patterns. A formal statistical test is proposed to decide whether two observed patterns share the same theoretical intensity model. This underlying model assumes that the first-order intensity function of the process generating the patterns may depend on covariate information. The test statistic consists of an L2-distance

    更新日期:2019-07-08
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