W. Meiring - Academia.edu (original) (raw)
Papers by W. Meiring
Statistica Sinica, 2005
Abstract: This article considers testing the hypothesis of Generalized Linear Models (GLM) versus... more Abstract: This article considers testing the hypothesis of Generalized Linear Models (GLM) versus general smoothing spline models for data from exponential families. The tests developed are based on the connection between smoothing spline models and Bayesian ...
Novartis Foundation Symposia, 2007
Accurate interpolation of soil and climate variables at fine spatial scales is necessary for prec... more Accurate interpolation of soil and climate variables at fine spatial scales is necessary for precise field management. Interpolation is needed to produce the input variables necessary for crop modelling. It is also important when deciding on regulations to limit environmental impacts from processes such as nitrate leaching. Non-stationarity may arise due to many factors, including differences in soil type, or heterogeneity in chemical concentrations. Many geostatistical methods make stationarity assumptions. Substantial improvements in interpolation or in the estimation of standard errors may be obtained by using non-stationary models of spatial covariances. This paper presents recent methodological developments for an approach to modelling non-stationary spatial covariance structure through deformations of the geographic coordinate system. This approach was first introduced by Sampson & Guttorp, although the estimation approach is updated in more recent papers. They compute a deformation of the geographic plane so that the spatial covariance structure can be considered stationary in terms of a new spatial coordinate system. This provides a non-stationary model for the spatial covariances between sampled locations and prediction locations. In this paper, we present a cross-validation procedure to avoid over-fitting of the sample dispersions. Results concerning the variability of the spatial covariance estimates are also presented. An example of the modelling of the spatial correlation field of rainfall at small regional scale is presented. Other directions in methodological development, including modelling temporally varying spatial correlation, and approaches to model temporal and spatial correlation are mentioned. Future directions for methodological development are indicated, including the modelling of multivariate processes and the use of external spatially dense covariables. Such covariates are frequently available in precision agriculture.
Journal of Applied Probability, 2003
We prove that a nonstationary random field indexed by ℝn with moments at least of order 2 can alw... more We prove that a nonstationary random field indexed by ℝn with moments at least of order 2 can always be viewed as second-order stationary in ℝ2n.
Environmetrics, 1994
We examine hourly ozone data collected in connection with a model evaluation study for ozone tran... more We examine hourly ozone data collected in connection with a model evaluation study for ozone transport in the San Joaquin Valley of California. A space-time analysis of a subset of the data, 17 sites concentrated around the Sacramento area, indicates a relatively simple spatial covariance structure at night-time, while the afternoon readings show a more complex spatial covariance, which is partly explained by observations from a single station with suspicious data. Simple separable space-time covariance models do not appear applicable to these data.
Journal of Agricultural, Biological and Environmental Statistics, 2018
Estimating the timing of the occurrence of events that characterize growth cycles in vegetation f... more Estimating the timing of the occurrence of events that characterize growth cycles in vegetation from time series of remote sensing data is desirable for a wide area of applications. For example, the timings of plant life cycle events are very sensitive to weather conditions, and are often used to assess the impacts of changes in weather and climate. Likewise, understanding crop phenology can have a large impact on agricultural strategies. To study phenology using remote sensing data, the timings of annual phenological events must be estimated from noisy time series that may have many missing values. Many current state-of-the-art methods consist of smoothing time series and estimating events as features of smoothed curves. A shortcoming of many of these methods is that they do not easily handle missing values and require imputation as a pre-processing step. In addition, while some currently used methods may be extendable to allow for temporal uncertainty quantification, uncertainty intervals are not usually provided with phenological event estimates. We propose methodology utilizing Bayesian dynamic linear models to estimate the timing of key phenological events from remote sensing data with uncertainty intervals. We illustrate the methodology on weekly vegetation index data from 2003 to 2007 over a region of southern India, focusing on estimating the timing of start of season (SOS) and peak of greenness (POG). Additionally, we present methods utilizing the Bayesian formulation and MCMC simulation of the model to estimate the probability that more than one growing season occurred in a given year.
This paper presents the most recent methodological developments for an approachto modelling nonst... more This paper presents the most recent methodological developments for an approachto modelling nonstationary spatial covariance structure through deformations ofthe geographic coordinate system that was first introduced in a technical report 10years ago (Sampson, 1986).We address primarily the problem of estimating the spatial covariance structurein levels of an environmental process at arbitrary locations (both monitored andunmonitored), based on records from N point monitoring sites x 1 ;...
Ciba Foundation symposium, 1997
Accurate interpolation of soil and climate variables at fine spatial scales is necessary for prec... more Accurate interpolation of soil and climate variables at fine spatial scales is necessary for precise field management. Interpolation is needed to produce the input variables necessary for crop modelling. It is also important when deciding on regulations to limit environmental impacts from processes such as nitrate leaching. Non-stationarity may arise due to many factors, including differences in soil type, or heterogeneity in chemical concentrations. Many geostatistical methods make stationarity assumptions. Substantial improvements in interpolation or in the estimation of standard errors may be obtained by using non-stationary models of spatial covariances. This paper presents recent methodological developments for an approach to modelling non-stationary spatial covariance structure through deformations of the geographic coordinate system. This approach was first introduced by Sampson & Guttorp, although the estimation approach is updated in more recent papers. They compute a defor...
Environmental and Ecological …, 1998
We present an approach to estimate hourly grid-cell surface ozone concentrations based on observa... more We present an approach to estimate hourly grid-cell surface ozone concentrations based on observations from point monitoring sites in space, for comparison with grid-based results from the SARMAP photochemical air-quality model for a region of northern California. ...
This paper presents the most recent methodological developments for an approachto modelling nonst... more This paper presents the most recent methodological developments for an approachto modelling nonstationary spatial covariance structure through deformations ofthe geographic coordinate system that was first introduced in a technical report 10years ago (Sampson, 1986).We address primarily the problem of estimating the spatial covariance structurein levels of an environmental process at arbitrary locations (both monitored andunmonitored), based on records from N point monitoring sites x 1 ;...
Environmetrics, 1994
We examine hourly ozone data collected in connection with a model evaluation study for ozone tran... more We examine hourly ozone data collected in connection with a model evaluation study for ozone transport in the San Joaquin Valley of California. A space-time analysis of a subset of the data, 17 sites concentrated around the Sacramento area, indicates a relatively simple spatial covariance structure at night-time, while the afternoon readings show a more complex spatial covariance, which is partly explained by observations from a single station with suspicious data. Simple separable space-time covariance models do not appear applicable to these data.
Mathematical biosciences, 2014
We present general methodology for sequential inference in nonlinear stochastic state-space model... more We present general methodology for sequential inference in nonlinear stochastic state-space models to simultaneously estimate dynamic states and fixed parameters. We show that basic particle filters may fail due to degeneracy in fixed parameter estimation and suggest the use of a kernel density approximation to the filtered distribution of the fixed parameters to allow the fixed parameters to regenerate. In addition, we show that "seemingly" uninformative uniform priors on fixed parameters can affect posterior inferences and suggest the use of priors bounded only by the support of the parameter. We show the negative impact of using multinomial resampling and suggest the use of either stratified or residual resampling within the particle filter. As a motivating example, we use a model for tracking and prediction of a disease outbreak via a syndromic surveillance system. Finally, we use this improved particle filtering methodology to relax prior assumptions on model paramete...
Journal of the American Statistical Association, 1997
... Paul D. Sampson is Research Associate Professor of Statistics, University of Washington, Seat... more ... Paul D. Sampson is Research Associate Professor of Statistics, University of Washington, Seattle, WA 98195. ... 3. THE RANDOM PROCESS: SPATIAL HETEROGENEITY The Houston area is geographically homogeneous and flat. Thus, if we can ignore local emission patterns, ...
Agricultural and Forest Meteorology, 2013
ABSTRACT The flow of carbon between terrestrial ecosystems and the atmosphere is mainly driven by... more ABSTRACT The flow of carbon between terrestrial ecosystems and the atmosphere is mainly driven by nonlinear, complex and time-lagged processes. Understanding the associated ecosystem responses is a key challenge regarding climate change questions such as the future development of the terrestrial carbon sink. However, high temporal resolution measurements of ecosystem variables (with the eddy covariance method) are subject to random error, that needs to be accounted for in model-data fusion, multi-site syntheses and up-scaling efforts. Gaussian Processes (GPs), a nonparametric regression method, have recently been shown to capture relationships in high-dimensional, nonlinear and noisy data. Heteroscedastic Gaussian Processes (HGPs) are a specialized GP method for data with inhomogeneous noise variance, such as eddy covariance measurements. Here, it is demonstrated that the HGP model captures measurement noise variances well, outperforming the model residual method and providing reasonable flux predictions at the same time. Based on meteorological drivers and temporal information, uncertainties of annual sums of carbon flux and water vapor flux at six different tower sites in Europe and North America are estimated. Similar noise patterns with different magnitudes were found across sites. Random uncertainties in annual sums of carbon fluxes were between 9.80 and 31.57 g C m−2 yr−1 (or 4–9% of the annual flux), and were between 2.54 and 8.13 mm yr−1 (or 1–2% of the annual flux) for water vapor fluxes. The empirical HGP model offers a general method to estimate random errors at half-hourly resolution based on entire annual records of measurements. It is introduced as a new tool for random uncertainty assessment widely throughout the FLUXNET network.
This paper presents the most recent methodological developments for an approachto modelling nonst... more This paper presents the most recent methodological developments for an approachto modelling nonstationary spatial covariance structure through deformations ofthe geographic coordinate system that was first introduced in a technical report 10years ago (Sampson, 1986).We address primarily the problem of estimating the spatial covariance structurein levels of an environmental process at arbitrary locations (both monitored andunmonitored), based on records from N point monitoring sites x 1 ;...
Statistica Sinica, 2005
Abstract: This article considers testing the hypothesis of Generalized Linear Models (GLM) versus... more Abstract: This article considers testing the hypothesis of Generalized Linear Models (GLM) versus general smoothing spline models for data from exponential families. The tests developed are based on the connection between smoothing spline models and Bayesian ...
Novartis Foundation Symposia, 2007
Accurate interpolation of soil and climate variables at fine spatial scales is necessary for prec... more Accurate interpolation of soil and climate variables at fine spatial scales is necessary for precise field management. Interpolation is needed to produce the input variables necessary for crop modelling. It is also important when deciding on regulations to limit environmental impacts from processes such as nitrate leaching. Non-stationarity may arise due to many factors, including differences in soil type, or heterogeneity in chemical concentrations. Many geostatistical methods make stationarity assumptions. Substantial improvements in interpolation or in the estimation of standard errors may be obtained by using non-stationary models of spatial covariances. This paper presents recent methodological developments for an approach to modelling non-stationary spatial covariance structure through deformations of the geographic coordinate system. This approach was first introduced by Sampson & Guttorp, although the estimation approach is updated in more recent papers. They compute a deformation of the geographic plane so that the spatial covariance structure can be considered stationary in terms of a new spatial coordinate system. This provides a non-stationary model for the spatial covariances between sampled locations and prediction locations. In this paper, we present a cross-validation procedure to avoid over-fitting of the sample dispersions. Results concerning the variability of the spatial covariance estimates are also presented. An example of the modelling of the spatial correlation field of rainfall at small regional scale is presented. Other directions in methodological development, including modelling temporally varying spatial correlation, and approaches to model temporal and spatial correlation are mentioned. Future directions for methodological development are indicated, including the modelling of multivariate processes and the use of external spatially dense covariables. Such covariates are frequently available in precision agriculture.
Journal of Applied Probability, 2003
We prove that a nonstationary random field indexed by ℝn with moments at least of order 2 can alw... more We prove that a nonstationary random field indexed by ℝn with moments at least of order 2 can always be viewed as second-order stationary in ℝ2n.
Environmetrics, 1994
We examine hourly ozone data collected in connection with a model evaluation study for ozone tran... more We examine hourly ozone data collected in connection with a model evaluation study for ozone transport in the San Joaquin Valley of California. A space-time analysis of a subset of the data, 17 sites concentrated around the Sacramento area, indicates a relatively simple spatial covariance structure at night-time, while the afternoon readings show a more complex spatial covariance, which is partly explained by observations from a single station with suspicious data. Simple separable space-time covariance models do not appear applicable to these data.
Journal of Agricultural, Biological and Environmental Statistics, 2018
Estimating the timing of the occurrence of events that characterize growth cycles in vegetation f... more Estimating the timing of the occurrence of events that characterize growth cycles in vegetation from time series of remote sensing data is desirable for a wide area of applications. For example, the timings of plant life cycle events are very sensitive to weather conditions, and are often used to assess the impacts of changes in weather and climate. Likewise, understanding crop phenology can have a large impact on agricultural strategies. To study phenology using remote sensing data, the timings of annual phenological events must be estimated from noisy time series that may have many missing values. Many current state-of-the-art methods consist of smoothing time series and estimating events as features of smoothed curves. A shortcoming of many of these methods is that they do not easily handle missing values and require imputation as a pre-processing step. In addition, while some currently used methods may be extendable to allow for temporal uncertainty quantification, uncertainty intervals are not usually provided with phenological event estimates. We propose methodology utilizing Bayesian dynamic linear models to estimate the timing of key phenological events from remote sensing data with uncertainty intervals. We illustrate the methodology on weekly vegetation index data from 2003 to 2007 over a region of southern India, focusing on estimating the timing of start of season (SOS) and peak of greenness (POG). Additionally, we present methods utilizing the Bayesian formulation and MCMC simulation of the model to estimate the probability that more than one growing season occurred in a given year.
This paper presents the most recent methodological developments for an approachto modelling nonst... more This paper presents the most recent methodological developments for an approachto modelling nonstationary spatial covariance structure through deformations ofthe geographic coordinate system that was first introduced in a technical report 10years ago (Sampson, 1986).We address primarily the problem of estimating the spatial covariance structurein levels of an environmental process at arbitrary locations (both monitored andunmonitored), based on records from N point monitoring sites x 1 ;...
Ciba Foundation symposium, 1997
Accurate interpolation of soil and climate variables at fine spatial scales is necessary for prec... more Accurate interpolation of soil and climate variables at fine spatial scales is necessary for precise field management. Interpolation is needed to produce the input variables necessary for crop modelling. It is also important when deciding on regulations to limit environmental impacts from processes such as nitrate leaching. Non-stationarity may arise due to many factors, including differences in soil type, or heterogeneity in chemical concentrations. Many geostatistical methods make stationarity assumptions. Substantial improvements in interpolation or in the estimation of standard errors may be obtained by using non-stationary models of spatial covariances. This paper presents recent methodological developments for an approach to modelling non-stationary spatial covariance structure through deformations of the geographic coordinate system. This approach was first introduced by Sampson & Guttorp, although the estimation approach is updated in more recent papers. They compute a defor...
Environmental and Ecological …, 1998
We present an approach to estimate hourly grid-cell surface ozone concentrations based on observa... more We present an approach to estimate hourly grid-cell surface ozone concentrations based on observations from point monitoring sites in space, for comparison with grid-based results from the SARMAP photochemical air-quality model for a region of northern California. ...
This paper presents the most recent methodological developments for an approachto modelling nonst... more This paper presents the most recent methodological developments for an approachto modelling nonstationary spatial covariance structure through deformations ofthe geographic coordinate system that was first introduced in a technical report 10years ago (Sampson, 1986).We address primarily the problem of estimating the spatial covariance structurein levels of an environmental process at arbitrary locations (both monitored andunmonitored), based on records from N point monitoring sites x 1 ;...
Environmetrics, 1994
We examine hourly ozone data collected in connection with a model evaluation study for ozone tran... more We examine hourly ozone data collected in connection with a model evaluation study for ozone transport in the San Joaquin Valley of California. A space-time analysis of a subset of the data, 17 sites concentrated around the Sacramento area, indicates a relatively simple spatial covariance structure at night-time, while the afternoon readings show a more complex spatial covariance, which is partly explained by observations from a single station with suspicious data. Simple separable space-time covariance models do not appear applicable to these data.
Mathematical biosciences, 2014
We present general methodology for sequential inference in nonlinear stochastic state-space model... more We present general methodology for sequential inference in nonlinear stochastic state-space models to simultaneously estimate dynamic states and fixed parameters. We show that basic particle filters may fail due to degeneracy in fixed parameter estimation and suggest the use of a kernel density approximation to the filtered distribution of the fixed parameters to allow the fixed parameters to regenerate. In addition, we show that "seemingly" uninformative uniform priors on fixed parameters can affect posterior inferences and suggest the use of priors bounded only by the support of the parameter. We show the negative impact of using multinomial resampling and suggest the use of either stratified or residual resampling within the particle filter. As a motivating example, we use a model for tracking and prediction of a disease outbreak via a syndromic surveillance system. Finally, we use this improved particle filtering methodology to relax prior assumptions on model paramete...
Journal of the American Statistical Association, 1997
... Paul D. Sampson is Research Associate Professor of Statistics, University of Washington, Seat... more ... Paul D. Sampson is Research Associate Professor of Statistics, University of Washington, Seattle, WA 98195. ... 3. THE RANDOM PROCESS: SPATIAL HETEROGENEITY The Houston area is geographically homogeneous and flat. Thus, if we can ignore local emission patterns, ...
Agricultural and Forest Meteorology, 2013
ABSTRACT The flow of carbon between terrestrial ecosystems and the atmosphere is mainly driven by... more ABSTRACT The flow of carbon between terrestrial ecosystems and the atmosphere is mainly driven by nonlinear, complex and time-lagged processes. Understanding the associated ecosystem responses is a key challenge regarding climate change questions such as the future development of the terrestrial carbon sink. However, high temporal resolution measurements of ecosystem variables (with the eddy covariance method) are subject to random error, that needs to be accounted for in model-data fusion, multi-site syntheses and up-scaling efforts. Gaussian Processes (GPs), a nonparametric regression method, have recently been shown to capture relationships in high-dimensional, nonlinear and noisy data. Heteroscedastic Gaussian Processes (HGPs) are a specialized GP method for data with inhomogeneous noise variance, such as eddy covariance measurements. Here, it is demonstrated that the HGP model captures measurement noise variances well, outperforming the model residual method and providing reasonable flux predictions at the same time. Based on meteorological drivers and temporal information, uncertainties of annual sums of carbon flux and water vapor flux at six different tower sites in Europe and North America are estimated. Similar noise patterns with different magnitudes were found across sites. Random uncertainties in annual sums of carbon fluxes were between 9.80 and 31.57 g C m−2 yr−1 (or 4–9% of the annual flux), and were between 2.54 and 8.13 mm yr−1 (or 1–2% of the annual flux) for water vapor fluxes. The empirical HGP model offers a general method to estimate random errors at half-hourly resolution based on entire annual records of measurements. It is introduced as a new tool for random uncertainty assessment widely throughout the FLUXNET network.
This paper presents the most recent methodological developments for an approachto modelling nonst... more This paper presents the most recent methodological developments for an approachto modelling nonstationary spatial covariance structure through deformations ofthe geographic coordinate system that was first introduced in a technical report 10years ago (Sampson, 1986).We address primarily the problem of estimating the spatial covariance structurein levels of an environmental process at arbitrary locations (both monitored andunmonitored), based on records from N point monitoring sites x 1 ;...