Papers by Montserrat Fuentes
North American Journal of Fisheries Management, Jan 9, 2011
An understanding of the spatial distribution of forage fish resources is required to make informe... more An understanding of the spatial distribution of forage fish resources is required to make informed fishery management decisions. We used mobile hydroacoustics to assess the distribution and abundance of forage fish in Badin Lake, a reservoir in central North Carolina. By sampling a series of cross-channel and longitudinal transects and analyzing the data using geostatistics, we characterized both large-and small-scale spatial patterns in forage fish density. Forage fish were observed in higher densities in upstream regions of the reservoir and were seen only in surface waters during July 2000 owing to the existence of a strong thermo-oxycline and in two layers (surface and near bottom) during mixed conditions in December 2001. We observed differences in the scale of patchiness (200-700 m) in forage fish distribution depending on the region of the reservoir where sampling took place, and we infer that these patterns are governed by prevailing limnological conditions. Modeling the spatial variation in the acoustic data using geostatistics resulted in similar average densities (July 2000: 0.56 Ϯ 0.28 [mean Ϯ SD] fish/m 2 ; December 2001: 0.57 Ϯ 0.49 fish/m 2 ) and improvements in the precision of abundance estimates based on approximated variance (
Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM 2.5 exposure and congenital heart defects
Statistics in Medicine, 2016
Epidemiologic studies suggest that maternal ambient air pollution exposure during critical period... more Epidemiologic studies suggest that maternal ambient air pollution exposure during critical periods of pregnancy is associated with adverse effects on fetal development. In this work, we introduce new methodology for identifying critical periods of development during post-conception gestational weeks 2-8 where elevated exposure to particulate matter less than 2.5 µm (PM2.5 ) adversely impacts development of the heart. Past studies have focused on highly aggregated temporal levels of exposure during the pregnancy and have failed to account for anatomical similarities between the considered congenital heart defects. We introduce a multinomial probit model in the Bayesian setting that allows for joint identification of susceptible daily periods during pregnancy for 12 types of congenital heart defects with respect to maternal PM2.5 exposure. We apply the model to a dataset of mothers from the National Birth Defect Prevention Study where daily PM2.5 exposures from post-conception gestational weeks 2-8 are assigned using predictions from the downscaler pollution model. This approach is compared with two aggregated exposure models that define exposure as the average value over post-conception gestational weeks 2-8 and the average over individual weeks, respectively. Results suggest an association between increased PM2.5 exposure on post-conception gestational day 53 with the development of pulmonary valve stenosis and exposures during days 50 and 51 with tetralogy of Fallot. Significant associations are masked when using the aggregated exposure models. Simulation study results suggest that the findings are robust to multiple sources of error. The general form of the model allows for different exposures and health outcomes to be considered in future applications. Copyright © 2016 John Wiley & Sons, Ltd.
Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM 2.5 exposure and congenital heart defects
Statistics in Medicine, 2016
Epidemiologic studies suggest that maternal ambient air pollution exposure during critical period... more Epidemiologic studies suggest that maternal ambient air pollution exposure during critical periods of pregnancy is associated with adverse effects on fetal development. In this work, we introduce new methodology for identifying critical periods of development during post-conception gestational weeks 2-8 where elevated exposure to particulate matter less than 2.5 µm (PM2.5 ) adversely impacts development of the heart. Past studies have focused on highly aggregated temporal levels of exposure during the pregnancy and have failed to account for anatomical similarities between the considered congenital heart defects. We introduce a multinomial probit model in the Bayesian setting that allows for joint identification of susceptible daily periods during pregnancy for 12 types of congenital heart defects with respect to maternal PM2.5 exposure. We apply the model to a dataset of mothers from the National Birth Defect Prevention Study where daily PM2.5 exposures from post-conception gestational weeks 2-8 are assigned using predictions from the downscaler pollution model. This approach is compared with two aggregated exposure models that define exposure as the average value over post-conception gestational weeks 2-8 and the average over individual weeks, respectively. Results suggest an association between increased PM2.5 exposure on post-conception gestational day 53 with the development of pulmonary valve stenosis and exposures during days 50 and 51 with tetralogy of Fallot. Significant associations are masked when using the aggregated exposure models. Simulation study results suggest that the findings are robust to multiple sources of error. The general form of the model allows for different exposures and health outcomes to be considered in future applications. Copyright © 2016 John Wiley & Sons, Ltd.
Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM 2.5 exposure and congenital heart defects
Statistics in Medicine, 2016
Epidemiologic studies suggest that maternal ambient air pollution exposure during critical period... more Epidemiologic studies suggest that maternal ambient air pollution exposure during critical periods of pregnancy is associated with adverse effects on fetal development. In this work, we introduce new methodology for identifying critical periods of development during post-conception gestational weeks 2-8 where elevated exposure to particulate matter less than 2.5 µm (PM2.5 ) adversely impacts development of the heart. Past studies have focused on highly aggregated temporal levels of exposure during the pregnancy and have failed to account for anatomical similarities between the considered congenital heart defects. We introduce a multinomial probit model in the Bayesian setting that allows for joint identification of susceptible daily periods during pregnancy for 12 types of congenital heart defects with respect to maternal PM2.5 exposure. We apply the model to a dataset of mothers from the National Birth Defect Prevention Study where daily PM2.5 exposures from post-conception gestational weeks 2-8 are assigned using predictions from the downscaler pollution model. This approach is compared with two aggregated exposure models that define exposure as the average value over post-conception gestational weeks 2-8 and the average over individual weeks, respectively. Results suggest an association between increased PM2.5 exposure on post-conception gestational day 53 with the development of pulmonary valve stenosis and exposures during days 50 and 51 with tetralogy of Fallot. Significant associations are masked when using the aggregated exposure models. Simulation study results suggest that the findings are robust to multiple sources of error. The general form of the model allows for different exposures and health outcomes to be considered in future applications. Copyright © 2016 John Wiley & Sons, Ltd.
Spatial Structure of the SeaWiFS Ocean Color Data for the North Atlantic Ocean
Lecture Notes in Statistics, 2000
Spatial Structure of the SeaWiFS Ocean Color Data for the North Atlantic Ocean
Lecture Notes in Statistics, 2000
Accounting for Design in the Analysis of Spatial Data
Advances in Efficient Data Acquisition, 2012
Recent technological advances have enabled researchers in a variety of fields to collect accurate... more Recent technological advances have enabled researchers in a variety of fields to collect accurately geocoded data for several variables simultaneously. In many cases it may be most appropriate to jointly model these multivariate spatial processes without constraints on their conditional relationships. When data have been collected on a regular lattice, the multivariate conditionally autoregressive (MCAR) models are a common choice. However, inference from these MCAR models relies heavily on the pre-specified neighborhood structure and often assumes a separable covariance structure. Here, we present a multivariate spatial model using a spectral analysis approach that enables inference on the conditional relationships between the variables that does not rely on a pre-specified neighborhood structure, is non-separable, and is computationally efficient. Covariance and cross-covariance functions are defined in the spectral domain to obtain computational efficiency. Posterior inference on the correlation matrix allows for quantification of the conditional dependencies. The approach is illustrated for the toxic element arsenic and four other soil elements whose relative concentrations were measured on a spatial lattice. Understanding conditional relationships between arsenic and other soil elements provides insights for mitigating poisoning in southern Asia and elsewhere.
Spectral Domain
Chapman & Hall/CRC Handbooks of Modern Statistical Methods, 2010
Constructing maps of pollution levels is vital for air quality management, and presents statistic... more Constructing maps of pollution levels is vital for air quality management, and presents statistical problems typical of many environmental and spatial applications. Ideally, such maps would be based on a dense network of monitoring stations, but this does not exist. Instead, there are two main sources of information in the U.S.: one is pollution measurements at a sparse set of about 50 monitoring stations called CASTNet, and the other is pollution emissions data. The pollution emissions data do not give direct information about pollution levels, but instead are combined with numerical models of weather and the emissions process and information about land use and cover (collectively called Models-3), to produce maps.
Nonparametric Bayesian models for a spatial covariance
Statistical Methodology, 2012
A crucial step in the analysis of spatial data is to estimate the spatial correlation function th... more A crucial step in the analysis of spatial data is to estimate the spatial correlation function that determines the relationship between a spatial process at two locations. The standard approach to selecting the appropriate correlation function is to use prior knowledge or exploratory analysis, such as a variogram analysis, to select the correct parametric correlation function. Rather that selecting a particular parametric correlation function, we treat the covariance function as an unknown function to be estimated from the data. We propose a flexible prior for the correlation function to provide robustness to the choice of correlation function. We specify the prior for the correlation function using spectral methods and the Dirichlet process prior, which is a common prior for an unknown distribution function. Our model does not require Gaussian data or spatial locations on a regular grid. The approach is demonstrated using a simulation study as well as an analysis of California air pollution data.
Influencia del marco de plantación en el rendimiento y calidad de dos cultivares de ajo ("Allium sativum" L.)
Comunicaciones V Jornadas Del Grupo De Horticultura Logrono 17 19 De Abril De 1996 1996 Isbn 84 8125 086 4 Pags 77 84, 1996
Annals of Applied Statistics, 2007
Storm surge, the onshore rush of sea water caused by the high winds and low pressure associated w... more Storm surge, the onshore rush of sea water caused by the high winds and low pressure associated with a hurricane, can compound the effects of inland flooding caused by rainfall, leading to loss of property and loss of life for residents of coastal areas. Numerical ocean models are essential for creating storm surge forecasts for coastal areas. These models are
ERA-40 project report series no
Nonstationary covariance models
Fusing deterministic models and data: A Bayesian multivariate spatial–temporal framework
Spatial Structure of the SeaWiFS Ocean Color Data for the North Atlantic Ocean
Statistica Sinica Preprint No: SS-13-240wR3
Spatial Statistics for Lattice Data
Spatial temporal modeling, estimation and prediction of environmental
Uploads
Papers by Montserrat Fuentes