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Efficient Statistical Mapping of Avian Count Data
(Environmental and Ecological Statistics, 2005)
We develop a spatial modeling framework for count data that is efficient to implement in high-dimensional prediction problems. We consider spectral parameterizations for the spatially varying mean of the Poisson model. The ...
Spatio-Temporal Hierarchical Bayesian Modeling: Tropical Ocean Surface Winds
(American Statistical Association, 2001)
Spatio-temporal processes are ubiquitous in the environmental and physical sciences. This is certainly true of atmospheric and oceanic processes, which typically exhibit many different scales of spatial and temporal ...
Predicting the Spatial Distribution of Ground Flora on Large Domains Using a Hierarchical Bayesian Model
(Landscape Ecology, 2003)
Accomodation of important sources of uncertainty in ecological models is essential to realistically predicting ecological processes. The purpose of this project is to develop a robust methodology for modeling natural ...
Universal Optimality in Balanced Uniform Crossover Design
(Institute of Mathematical Statistics, 2003)
Kunert [Ann. Statist. 12 (1984) 1006-1017] proved that, in the class of repeated measurement designs based on t treatments, p = t periods and n = λt experimental units, a balanced uniform design is universally optimal
for ...
A Kernel-Based Spectral Model for Non-Gaussian Spatio-Temporal Processes
(Statistical Modelling, 2002)
Spatio-temporal processes can often be written as hierarchical state-space processes. In situations with complicated dynamics such as wave propagation, it is difficult to parameterize state transition functions for ...
Multiresolution Models for Nonstationary Spatial Covariance Functions
(Statistical Modelling, 2002)
Many geophysical and environmental problems depend on estimating a spatial process that has nonstationary structure. A nonstationary model is proposed based on the spatial field being a linear combination of a multiresolution ...
Strong Consistency of MLE in Nonlinear Mixed-effects Models with Large Cluster Size
(Indian Statistical Institute, 2005)
The search for conditions for the consistency of maximum likelihood estimators in nonlinear
mixed effects models is difficult due to the fact that, in general, the likelihood can
only be expressed as an integral over the ...
Accounting for Uncertainty in Ecological Analysis: The Strengths and Limitations of Hierarchical Statistical Modeling
(Ecological Society of America, 2009)
Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since
ecology is known for its complexity. ...
Bayesian semiparametric spatial and joint spatio-temporal modeling
(University of Missouri--Columbia, 2006)
Over the past decades a great deal of effort has been expended in the collection and compilation of high quality data on cancer incidence and mortality in the United States. These data have largely been used in the creation ...
Hierarchical spatio-temporal models for ecological processes
(University of Missouri--Columbia, 2006)
Ecosystems are composed of phenomena that propagate in time and space. Often, ecological processes underlying such phenomena are studied separably in various subdisciplines, while larger scale, interlinking mechanisms are ...
Hierarchical spatio-temporal models for environmental processes
(University of Missouri--Columbia, 2007)
The processes governing environmental systems are often complex, involving different interacting scales of variability in space and time. The complexities and often high dimensionality of such spatio-temporal processes can ...
Hierarchical Bayesian Models for Predicting The Spread of Ecological Processes
(Ecological Society of America, 2003)
There is increasing interest in predicting ecological processes. Methods to accomplish such predictions must account for uncertainties in observation, sampling,
models, and parameters. Statistical methods for spatio-temporal ...
Bayesian smoothing spline analysis of variance models
(University of Missouri--Columbia, 2009)
Based on the pioneering work by Wahba (1990) in smoothing splines for nonparametric regression, Gu (2002) decomposed the regression function based on a tensor sum decomposition of inner product spaces into orthogonal ...
A Bayesian Approach to Estimating the Long Memory Parameter
(Bayesian Analysis, 2009)
We develop a Bayesian procedure for analyzing stationary long-range dependent processes. Specifically, we consider the fractional exponential model (FEXP) to estimate the memory parameter of a stationary long-memory Gaussian ...
Hierarchical Bayesian Approach to Boundary Value Problems with Stochastic Boundary Conditions
(American Meteorological Society, 2003)
Boundary value problems are ubiquitous in the atmospheric and ocean sciences. Typical settings include bounded, partially bounded, global and limited area domains, discretized for applications of numerical models of
the ...
Shifts in the Spatio-Temporal Growth Dynamics of Shortleaf Pine
(Environmental and Ecological Statistics, 2007)
Previous studies focusing on the growth history of pinus echinata at the edge of its geographical range have suggested that changes in growth correspond to climatic and non-climatic (e.g., anthropogenic) factors. We employ ...
ComPhy: Prokaryotic Composite Distance Phylogenies Inferred from Whole-Genome Gene Sets
(BioMed Central, 2009)
With the increasing availability of whole genome sequences, it is becoming more and more important to use complete genome sequences for inferring species phylogenies. We developed a new tool ComPhy, 'Composite Distance ...
Population Influences on Tornado Reports in the United States
(Weather and Forecasting, 2005)
The number of tornadoes reported in the United States is believed to be less than the actual incidence of tornadoes, especially prior to the 1990s, because tornadoes may be undetectable by human witnesses in sparsely ...
Bayesian spatial data analysis with application to the Missouri Ozark forest ecosystem project
(University of Missouri--Columbia, 2006)
The first part studies the problem of estimating the covariance matrix in a star-shaped model with missing data. By introducing a class of priors based on a type of Cholesky decomposition of the precision matrix, we then ...
Statistical analysis of multivariate interval-censored failure time data
(University of Missouri--Columbia, 2006)
Interval-censored failure time data commonly arise in clinical trials and medical studies. In such studies, the failure time of interest is often not exactly observed, but known to fall within some interval. For multivariate ...