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Items from 1 to 20 out of 594 results

article

A Bayesian spatial model for neuroimaging data based on biologically informed basis functions

Ismael Huertas, Marianne Oldehinkel, Erik S.B. van Oort, David Garcia-Solis, more

NeuroImage > 2017 > 161 > C > 134-148

The dominant approach to neuroimaging data analysis employs the voxel as the unit of computation. While convenient, voxels lack biological meaning and their size is arbitrarily determined by the resolution of the image. Here, we propose a multivariate spatial model in which neuroimaging data are characterised as a linearly weighted combination of multiscale basis functions which map onto underlying...

article

Corticosterone Metabolite Concentrations in Greater Sage-Grouse Are Positively Associated With the Presence of Cattle Grazing

M.D. Jankowski, R.E. Russell, J.C. Franson, R.J. Dusek, more

Rangeland Ecology & Management > 2014 > 67 > 3 > 237-246

The sagebrush biome in the western United States is home to the imperiled greater sage-grouse (Centrocercus urophasianus) and encompasses rangelands used for cattle production. Cattle grazing activities have been implicated in the range-wide decline of the sage-grouse, but no studies have investigated the relationship between the physiological condition of sage-grouse and the presence of grazing cattle...

article

Quantifying the scale of gravel-bed clusters with spatial statistics

Philip L'Amoreaux, Stanford Gibson, P. L'Amoreaux, S. Gibson

Geomorphology > 2013 > 197 > Complete > 56-63

Clustering of large surficial particles affect the critical shear stress, bedload transport rate, and ecological processes of gravel rivers. Particle clusters have been the subject of careful study and taxonomy but would benefit from new quantitative, reproducible, statistical analysis, particularly in the gravel size classes where clusters are difficult to reliably resolve by visual, gestalt methods...

article

Bayesian modeling of spatial ordinal data from health surveys

Miguel Ángel Beltrán‐Sánchez, Miguel‐Angel Martinez‐Beneito, Ana Corberán‐Vallet

Statistics in Medicine > 43 > 21 > 4178 - 4193

Health surveys allow exploring health indicators that are of great value from a public health point of view and that cannot normally be studied from regular health registries. These indicators are usually coded as ordinal variables and may depend on covariates associated with individuals. In this article, we propose a Bayesian individual‐level model for small‐area estimation of survey‐based health...

article

Smoothed model‐assisted small area estimation of proportions

Peter A. Gao, Jon Wakefield

Canadian Journal of Statistics > 52 > 2 > 337 - 358

In countries where population census data are limited, generating accurate subnational estimates of health and demographic indicators is challenging. Existing model‐based geostatistical methods leverage covariate information and spatial smoothing to reduce the variability of estimates but often ignore the survey design, while traditional small area estimation approaches may not incorporate both unit‐level...

article

Distributional ecology: Opening new research windows by addressing aggregation‐related puzzles

Youhua Chen, Tsung‐Jen Shen

Diversity and Distributions > 30 > 4 > n/a - n/a

Aim Distribution of species is one of the most elementary but fundamental biodiversity patterns, yet too many puzzles remain unsolved. In most cases, distribution of species is not random, but presents some degree of aggregation. Accordingly, the term ‘distributional aggregation’ is prevailingly used in ecology and evolutionary biology to reflect the non‐random distribution characteristic of species...

article

Modeling variation in mixture effects over space with a Bayesian spatially varying mixture model

Joseph Boyle, Mary H. Ward, James R. Cerhan, Nat Rothman, more

Statistics in Medicine > 43 > 7 > 1441 - 1457

Mixture analysis is an emerging statistical tool in epidemiological research that seeks to estimate the health effects associated with mixtures of several exposures. This approach acknowledges that individuals experience many simultaneous exposures and it can estimate the relative importance of components in the mixture. Health effects due to mixtures may vary over space driven by to political, demographic,...

article

Spatial modeling of Mycobacterium tuberculosis transmission with dyadic genetic relatedness data

Joshua L. Warren, Melanie H. Chitwood, Benjamin Sobkowiak, Caroline Colijn, more

Biometrics > 79 > 4 > 3650 - 3663

Understanding factors that contribute to the increased likelihood of pathogen transmission between two individuals is important for infection control. However, analyzing measures of pathogen relatedness to estimate these associations is complicated due to correlation arising from the presence of the same individual across multiple dyadic outcomes, potential spatial correlation caused by unmeasured...

article

Variational log‐Gaussian point‐process methods for grid cells

Michael Everett Rule, Prannoy Chaudhuri‐Vayalambrone, Marino Krstulovic, Marius Bauza, more

Hippocampus > 33 > 12 > 1235 - 1251

We present practical solutions to applying Gaussian‐process (GP) methods to calculate spatial statistics for grid cells in large environments. GPs are a data efficient approach to inferring neural tuning as a function of time, space, and other variables. We discuss how to design appropriate kernels for grid cells, and show that a variational Bayesian approach to log‐Gaussian Poisson models can be...

article

A Spatial Variance‐Smoothing Area Level Model for Small Area Estimation of Demographic Rates

Peter A. Gao, Jonathan Wakefield

International Statistical Review > 91 > 3 > 493 - 510

Accurate estimates of subnational health and demographic indicators are critical for informing policy. Many countries collect relevant data using complex household surveys, but when data are limited, direct weighted estimates of small area proportions may be unreliable. Area level models treating these direct estimates as response data can improve precision but often require known sampling variances...

article

Banana bunchy top disease in Africa—Predicting continent‐wide disease risks by combining survey data and expert knowledge

Hein Bouwmeester, Guy Blomme, Aman Bonaventure Omondi, Walter Ocimati

Plant Pathology > 72 > 8 > 1476 - 1490

Across Africa, banana bunchy top disease (BBTD) severely impacts banana production and livelihoods of millions of smallholder farmers. Mapping vulnerability of landscapes to monitor BBTD establishment and spread is crucial for proactive measures of disease exclusion. To highlight current and future risks of BBTD in Africa, the relationship between 1160 field observations from 14 BBTD surveys and environmental...

article

Spatial analyses of immune cell infiltration in cancer: current methods and future directions. A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer

David B Page, Glenn Broeckx, Chowdhury Arif Jahangir, Sara Verbandt, more

The Journal of Pathology > 260 > 5 > 514 - 532

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting...

article

Diseases maps of spatial epidemiological data by R

Takafumi Kubota

Wiley Interdisciplinary Reviews: Computational Statistics > 15 > 4 > n/a - n/a

Disease maps are essential when analyzing spatial epidemiological data, such as newly detected COVID‐19 positive cases or suicide deaths, because it is necessary to determine the method of analysis in order to perform spatial statistical analysis. Disease maps give an initial overview of the data and provide evidence of regional trends, which the analyst can check. Therefore, in this article, the...

article

A novel Bayesian functional spatial partitioning method with application to prostate cancer lesion detection using MRI

Maria Masotti, Lin Zhang, Ethan Leng, Gregory J. Metzger, more

Biometrics > 79 > 2 > 604 - 615

Spatial partitioning methods correct for nonstationarity in spatially related data by partitioning the space into regions of local stationarity. Existing spatial partitioning methods can only estimate linear partitioning boundaries. This is inadequate for detecting an arbitrarily shaped anomalous spatial region within a larger area. We propose a novel Bayesian functional spatial partitioning (BFSP)...

article

A model of spatial location: New data for the Gor River megalithic landscape (Spain) from LiDAR technology and field survey

Carolina Cabrero‐González, Antonio Garrido‐Almonacid, Francisco Javier Esquivel, Juan Antonio Cámara‐Serrano

Archaeological Prospection > 30 > 2 > 89 - 103

The megalithic cluster of the Gor River valley (Andalusia, Spain) is one of the biggest dolmenic groups in Europe, made up of 151 preserved megaliths. In spite of this high number of known monuments, increasing loss and destruction of many of the graves has taken place during the last decades due to enormous soil erosion and anthropogenic activities. With the aim of recording the location of these...

article

Spatiotemporal analysis of Phytophthora infestans population diversity and disease risk in Great Britain

Araceli Torro‐Galiana, David E. L. Cooke, Peter Skelsey

Plant Pathology > 72 > 4 > 786 - 796

Phytophthora infestans (causal agent of potato late blight) populations are mainly clonal in Great Britain (GB), but sexual reproduction of the pathogen leads to the emergence of novel (non‐clonal) genotypes that may have traits such as new virulence or fungicide insensitivity that can make them more challenging to control. In this study, spatiotemporal variation in diversity of P. infestans across...

article

Are You All Normal? It Depends!

Wanfang Chen, Marc G. Genton

International Statistical Review > 91 > 1 > 114 - 139

The assumption of normality has underlain much of the development of statistics, including spatial statistics, and many tests have been proposed. In this work, we focus on the multivariate setting and first review the recent advances in multivariate normality tests for i.i.d. data, with emphasis on the skewness and kurtosis approaches. We show through simulation studies that some of these tests cannot...

article

Contour models for physical boundaries enclosing star‐shaped and approximately star‐shaped polygons

Hannah M. Director, Adrian E. Raftery

Journal of the Royal Statistical Society: Series C (Applied Statistics) > 71 > 5 > 1688 - 1720

Boundaries on spatial fields divide regions with particular features from surrounding background areas. Methods to identify boundary lines from interpolated spatial fields are well established. Less attention has been paid to how to model sequences of connected spatial points. Such models are needed for physical boundaries. For example, in the Arctic ocean, large contiguous areas are covered by sea...

article

Functional peaks‐over‐threshold analysis

Raphaël de Fondeville, Anthony C. Davison

Journal of the Royal Statistical Society: Series B (Statistical Methodology) > 84 > 4 > 1392 - 1422

Peaks‐over‐threshold analysis using the generalised Pareto distribution is widely applied in modelling tails of univariate random variables, but much information may be lost when complex extreme events are studied using univariate results. In this paper, we extend peaks‐over‐threshold analysis to extremes of functional data. Threshold exceedances defined using a functional r are modelled by the generalised...

article

Nearest‐neighbor sparse Cholesky matrices in spatial statistics

Abhirup Datta

Wiley Interdisciplinary Reviews: Computational Statistics > 14 > 5 > n/a - n/a

Gaussian process (GP) is a staple in the toolkit of a spatial statistician. Well‐documented computing roadblocks in the analysis of large geospatial datasets using GPs have now largely been mitigated via several recent statistical innovations. Nearest neighbor Gaussian process (NNGP) has emerged as one of the leading candidates for such massive‐scale geospatial analysis owing to their empirical success...

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Search results

A Bayesian spatial model for neuroimaging data based on biologically informed basis functions

Corticosterone Metabolite Concentrations in Greater Sage-Grouse Are Positively Associated With the Presence of Cattle Grazing

Quantifying the scale of gravel-bed clusters with spatial statistics

Bayesian modeling of spatial ordinal data from health surveys

Smoothed model‐assisted small area estimation of proportions

Distributional ecology: Opening new research windows by addressing aggregation‐related puzzles

Modeling variation in mixture effects over space with a Bayesian spatially varying mixture model

Spatial modeling of Mycobacterium tuberculosis transmission with dyadic genetic relatedness data

Variational log‐Gaussian point‐process methods for grid cells

A Spatial Variance‐Smoothing Area Level Model for Small Area Estimation of Demographic Rates

Banana bunchy top disease in Africa—Predicting continent‐wide disease risks by combining survey data and expert knowledge

Spatial analyses of immune cell infiltration in cancer: current methods and future directions. A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer

Diseases maps of spatial epidemiological data by R

A novel Bayesian functional spatial partitioning method with application to prostate cancer lesion detection using MRI

A model of spatial location: New data for the Gor River megalithic landscape (Spain) from LiDAR technology and field survey

Spatiotemporal analysis of Phytophthora infestans population diversity and disease risk in Great Britain

Are You All Normal? It Depends!

Contour models for physical boundaries enclosing star‐shaped and approximately star‐shaped polygons

Functional peaks‐over‐threshold analysis

Nearest‐neighbor sparse Cholesky matrices in spatial statistics

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