Results 11 to 20 of about 518,510 (312)
European Journal of Soil Science, 2010 Geostatistical analysis of soil properties is undertaken to allow prediction of values of these properties over regions or at unsampled locations. A key step in geostatistical analysis is the estimation of a variogram function that describes the spatial covariance structure of the variable in question. If it can be assumed plausibly that the data are a
R. M. Lark, Lark, R. M.
core +3 more sources
PLoS ONE, 2017 Recently-developed methods that integrate multiple data sources arising from the same ecological processes have typically utilized structured data from well-defined sampling protocols (e.g., capture-recapture and telemetry).
Simone Tenan +4 more
doaj +2 more sources
Inference for spatial processes using imperfect data from measurements and numerical simulations
, 2016 We present a framework for inference for spatial processes that have actual values imperfectly represented by data. Environmental processes represented as spatial fields, either at fixed time points, or aggregated over fixed time periods, are studied.
Youngman, BD, Stephenson, DB
core +4 more sources
Spatial and spatio-temporal Log-Gaussian Cox processes : extending the geostatistical paradigm [PDF]
, 2013 In this paper we first describe the class of log-Gaussian Cox processes (LGCPs) as models for spatial and spatio-temporal point process data. We discuss inference, with a particular focus on the computational challenges of likelihood-based inference.
Rowlingson, Barry +6 more
core +2 more sources
Distributed Inference for Spatial Extremes Modeling in High Dimensions [PDF]
Journal of the American Statistical Association, 2022 Extreme environmental events frequently exhibit spatial and temporal dependence. These data are often modeled using Max Stable Processes (MSPs) that are computationally prohibitive to fit for as few as a dozen observations.
Emily C. Hector, B. Reich
semanticscholar +1 more source
Non-parametric spatial curvature inference using late-Universe cosmological probes [PDF]
, 2021 Inferring high-fidelity constraints on the spatial curvature parameter, ΩK, under as few assumptions as possible, is of fundamental importance in cosmology. We propose a method to non-parametrically infer ΩK from late-Universe probes alone.
S. Dhawan, J. Alsing, S. Vagnozzi
semanticscholar +1 more source
Bayesian Modeling with Spatial Curvature Processes [PDF]
Journal of the American Statistical Association, 2023 Spatial process models are widely used for modeling point-referenced variables arising from diverse scientific domains. Analyzing the resulting random surface provides deeper insights into the nature of latent dependence within the studied response.
Aritra Halder, S. Banerjee, D. Dey
semanticscholar +1 more source
bioRxiv, 2023 Spatial transcriptomics (ST) technology, providing spatially resolved transcriptional profiles, facilitates advanced understanding of key biological processes related to health and disease.
Jiawen Chen +5 more
semanticscholar +1 more source
Bayesian inference for big spatial data using non-stationary spectral simulation [PDF]
Spatial Statistics, 2020 It is increasingly understood that the assumption of stationarity is unrealistic for many spatial processes. In this article, we combine dimension expansion with a spectral method to model big non-stationary spatial fields in a computationally efficient ...
Hou‐Cheng Yang, J. Bradley
semanticscholar +1 more source
Modeling Spatial Processes with Unknown Extremal Dependence Class [PDF]
Journal of the American Statistical Association, 2017 Many environmental processes exhibit weakening spatial dependence as events become more extreme. Well-known limiting models, such as max-stable or generalized Pareto processes, cannot capture this, which can lead to a preference for models that exhibit a
Raphael Huser, J. Wadsworth
semanticscholar +1 more source