The Experts below are selected from a list of 223752 Experts worldwide ranked by ideXlab platform
Christopher K Wikle - One of the best experts on this subject based on the ideXlab platform.
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Spatio-Temporal Change of support modeling with R
Computational Statistics, 2020Co-Authors: Andrew M Raim, Scott H Holan, Jonathan R Bradley, Christopher K WikleAbstract:Spatio-Temporal Change of support methods are designed for statistical analysis on spatial and Temporal domains which can differ from those of the observed data. Previous work introduced a parsimonious class of Bayesian hierarchical spatio-Temporal models, which we refer to as STCOS, for the case of Gaussian outcomes. Application of STCOS methodology from this literature requires a level of proficiency with spatio-Temporal methods and statistical computing which may be a hurdle for potential users. The present work seeks to bridge this gap by guiding readers through STCOS computations. We focus on the R computing environment because of its popularity, free availability, and high quality contributed packages. The stcos package is introduced to facilitate computations for the STCOS model. A motivating application is the American Community Survey (ACS), an ongoing survey administered by the U.S. Census Bureau that measures key socioeconomic and demographic variables for various populations in the United States. The STCOS methodology offers a principled approach to compute model-based estimates and associated measures of uncertainty for ACS variables on customized geographies and/or time periods. We present a detailed case study with ACS data as a guide for Change of support analysis in R , and as a foundation which can be customized to other applications.
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an r package for spatio Temporal Change of support
arXiv: Computation, 2019Co-Authors: Andrew M Raim, Scott H Holan, Jonathan R Bradley, Christopher K WikleAbstract:Spatio-Temporal Change of support (STCOS) methods are designed for statistical inference and prediction on spatial and/or Temporal domains which differ from the domains on which the data were observed. Bradley, Wikle, and Holan (2015; Stat) introduced a parsimonious class of Bayesian hierarchical spatio-Temporal models for STCOS for Gaussian data through a motivating application involving the American Community Survey (ACS), an ongoing survey administered by the U.S. Census Bureau that measures key socioeconomic and demographic variables for various populations in the United States. Importantly, their methodology provides ACS data-users a principled approach to estimating variables of interest, along with associated measures of uncertainty, on customized geographies and/or time periods. In this work, we develop an R package to make the methodology broadly accessible to federal statistical agencies, such as the Census Bureau, the ACS data-user community, and to the general R-user community. The package is illustrated through a detailed case-study based on real data.
Kjell Johansson - One of the best experts on this subject based on the ideXlab platform.
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Temporal Change estimation of mercury concentrations in northern pike esox lucius l in swedish lakes
Chemosphere, 2012Co-Authors: Staffan Åkerblom, Mats Nilsson, Bo Ranneby, Jun Yu, Kjell JohanssonAbstract:Adequate Temporal trend analysis of mercury (Hg) in freshwater ecosystems is critical to evaluate if actions from the human society have affected Hg concentrations ([Hg]) in fresh water biota. This ...
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Temporal Change estimation of mercury concentrations in northern pike (Esox lucius L.) in Swedish lakes.
Chemosphere, 2011Co-Authors: Staffan Åkerblom, Mats Nilsson, Bo Ranneby, Kjell JohanssonAbstract:Adequate Temporal trend analysis of mercury (Hg) in freshwater ecosystems is critical to evaluate if actions from the human society have affected Hg concentrations ([Hg]) in fresh water biota. This study examined Temporal Change in [Hg] in Northern pike (Esox lucius L.) in Swedish freshwater lakes between 1994 and 2006. To achieve this were lake-specific, multiple-linear-regression models used to estimate pike [Hg], including indicator variables representing time and fish weight and their interactions. This approach permitted estimation of the direction and magnitude of Temporal Changes in 25 lakes selected from the Swedish national database on Hg in freshwater biota. A significant increase was found in 36% of the studied lakes with an average increase in pike [Hg] of 3.7±6.7% per year that was found to be positively correlated with total organic carbon. For lakes with a significant Temporal Change the dataset was based on a mean of 30 fish, while for lakes with no Temporal Change it was based on a mean of 13 fish.
Andrew M Raim - One of the best experts on this subject based on the ideXlab platform.
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Spatio-Temporal Change of support modeling with R
Computational Statistics, 2020Co-Authors: Andrew M Raim, Scott H Holan, Jonathan R Bradley, Christopher K WikleAbstract:Spatio-Temporal Change of support methods are designed for statistical analysis on spatial and Temporal domains which can differ from those of the observed data. Previous work introduced a parsimonious class of Bayesian hierarchical spatio-Temporal models, which we refer to as STCOS, for the case of Gaussian outcomes. Application of STCOS methodology from this literature requires a level of proficiency with spatio-Temporal methods and statistical computing which may be a hurdle for potential users. The present work seeks to bridge this gap by guiding readers through STCOS computations. We focus on the R computing environment because of its popularity, free availability, and high quality contributed packages. The stcos package is introduced to facilitate computations for the STCOS model. A motivating application is the American Community Survey (ACS), an ongoing survey administered by the U.S. Census Bureau that measures key socioeconomic and demographic variables for various populations in the United States. The STCOS methodology offers a principled approach to compute model-based estimates and associated measures of uncertainty for ACS variables on customized geographies and/or time periods. We present a detailed case study with ACS data as a guide for Change of support analysis in R , and as a foundation which can be customized to other applications.
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an r package for spatio Temporal Change of support
arXiv: Computation, 2019Co-Authors: Andrew M Raim, Scott H Holan, Jonathan R Bradley, Christopher K WikleAbstract:Spatio-Temporal Change of support (STCOS) methods are designed for statistical inference and prediction on spatial and/or Temporal domains which differ from the domains on which the data were observed. Bradley, Wikle, and Holan (2015; Stat) introduced a parsimonious class of Bayesian hierarchical spatio-Temporal models for STCOS for Gaussian data through a motivating application involving the American Community Survey (ACS), an ongoing survey administered by the U.S. Census Bureau that measures key socioeconomic and demographic variables for various populations in the United States. Importantly, their methodology provides ACS data-users a principled approach to estimating variables of interest, along with associated measures of uncertainty, on customized geographies and/or time periods. In this work, we develop an R package to make the methodology broadly accessible to federal statistical agencies, such as the Census Bureau, the ACS data-user community, and to the general R-user community. The package is illustrated through a detailed case-study based on real data.
Christo Pantev - One of the best experts on this subject based on the ideXlab platform.
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Auditory evoked fields elicited by spectral, Temporal, and spectral-Temporal Changes in human cerebral cortex.
Frontiers in psychology, 2012Co-Authors: Hidehiko Okamoto, Henning Teismann, Ryusuke Kakigi, Christo PantevAbstract:Natural sounds contain complex spectral components, which are Temporally modulated as time-varying signals. Recent studies have suggested that the auditory system encodes spectral and Temporal sound information differently. However, it remains unresolved how the human brain processes sounds containing both spectral and Temporal Changes. In the present study, we investigated human auditory evoked responses elicited by spectral, Temporal, and spectral-Temporal sound Changes by means of magnetoencephalography (MEG). The auditory evoked responses elicited by the spectral-Temporal Change were very similar to those elicited by the spectral Change, but those elicited by the Temporal Change were delayed by 30 – 50 ms and differed from the others in morphology. The results suggest that human brain responses corresponding to spectral sound Changes precede those corresponding to Temporal sound Changes, even when the spectral and Temporal Changes occur simultaneously.
Staffan Åkerblom - One of the best experts on this subject based on the ideXlab platform.
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Temporal Change estimation of mercury concentrations in northern pike esox lucius l in swedish lakes
Chemosphere, 2012Co-Authors: Staffan Åkerblom, Mats Nilsson, Bo Ranneby, Jun Yu, Kjell JohanssonAbstract:Adequate Temporal trend analysis of mercury (Hg) in freshwater ecosystems is critical to evaluate if actions from the human society have affected Hg concentrations ([Hg]) in fresh water biota. This ...
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Temporal Change estimation of mercury concentrations in northern pike (Esox lucius L.) in Swedish lakes.
Chemosphere, 2011Co-Authors: Staffan Åkerblom, Mats Nilsson, Bo Ranneby, Kjell JohanssonAbstract:Adequate Temporal trend analysis of mercury (Hg) in freshwater ecosystems is critical to evaluate if actions from the human society have affected Hg concentrations ([Hg]) in fresh water biota. This study examined Temporal Change in [Hg] in Northern pike (Esox lucius L.) in Swedish freshwater lakes between 1994 and 2006. To achieve this were lake-specific, multiple-linear-regression models used to estimate pike [Hg], including indicator variables representing time and fish weight and their interactions. This approach permitted estimation of the direction and magnitude of Temporal Changes in 25 lakes selected from the Swedish national database on Hg in freshwater biota. A significant increase was found in 36% of the studied lakes with an average increase in pike [Hg] of 3.7±6.7% per year that was found to be positively correlated with total organic carbon. For lakes with a significant Temporal Change the dataset was based on a mean of 30 fish, while for lakes with no Temporal Change it was based on a mean of 13 fish.
