Statistics publications (MU)The items in this collection are the scholarly output of the faculty, staff, and students of the Department of Statistics.https://hdl.handle.net/10355/90682022-12-10T09:41:42Z2022-12-10T09:41:42ZAccounting for Uncertainty in Ecological Analysis: The Strengths and Limitations of Hierarchical Statistical ModelingCressie, Noel A. C.Calder, Catherine A., 1976-Clark, James Samuel, 1957-Ver Hoef, Jay M.Wikle, Christopher K., 1963-https://hdl.handle.net/10355/90692019-05-29T15:54:29Z2009-01-01T00:00:00ZAccounting for Uncertainty in Ecological Analysis: The Strengths and Limitations of Hierarchical Statistical Modeling
Cressie, Noel A. C.; Calder, Catherine A., 1976-; Clark, James Samuel, 1957-; Ver Hoef, Jay M.; Wikle, Christopher K., 1963-
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. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This
approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian)
in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.
Copyright by the Ecological Society of America.
2009-01-01T00:00:00ZA Bayesian Approach to Estimating the Long Memory ParameterHolan, ScottMcElroy, TuckerChakraborty, Sounakhttps://hdl.handle.net/10355/91242019-05-16T14:38:00Z2009-01-01T00:00:00ZA Bayesian Approach to Estimating the Long Memory Parameter
Holan, Scott; McElroy, Tucker; Chakraborty, Sounak
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 time series. In particular, we propose a hierarchical Bayesian model and make
it fully adaptive by imposing a prior distribution on the model order. Further,
we describe a reversible jump Markov chain Monte Carlo algorithm for variable dimension estimation and show that, in our context, the algorithm provides a reasonable method of model selection (within each repetition of the chain). Therefore, through an application of Bayesian model averaging, we incorporate all possible models from the FEXP class (up to a given finite order). As a result we reduce the
underestimation of uncertainty at the model-selection stage as well as achieve better estimates of the long memory parameter. Additionally, we establish Bayesian consistency of the memory parameter under mild conditions on the data process. Finally, through simulation and the analysis of two data sets, we demonstrate the effectiveness of our approach.
DOI:10.1214/09-BA406
2009-01-01T00:00:00ZComPhy: Prokaryotic Composite Distance Phylogenies Inferred from Whole-Genome Gene SetsLin, Guan Ning, 1978-Cai, ZhipengLin, GuohuiChakraborty, SounakXu, Dong, 1965-https://hdl.handle.net/10355/91262019-05-16T14:38:01Z2009-01-01T00:00:00ZComPhy: Prokaryotic Composite Distance Phylogenies Inferred from Whole-Genome Gene Sets
Lin, Guan Ning, 1978-; Cai, Zhipeng; Lin, Guohui; Chakraborty, Sounak; Xu, Dong, 1965-
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 Phylogeny', based on a composite distance matrix calculated from the comparison of complete gene sets between genome pairs to produce a prokaryotic phylogeny. The composite distance between two genomes is defined by three components: Gene Dispersion Distance (GDD), Genome Breakpoint Distance (GBD) and Gene Content Distance (GCD). GDD quantifies the dispersion of orthologous genes along the genomic coordinates from one genome to another; GBD measures the shared breakpoints between two genomes; GCD measures the level of shared orthologs between two genomes. The
phylogenetic tree is constructed from the composite distance matrix using a neighbor
joining method. We tested our method on 9 datasets from 398 completely sequenced
prokaryotic genomes. We have achieved above 90% agreement in quartet topologies between the tree created by our method and the tree from the Bergey's taxonomy. In comparison to several other phylogenetic analysis methods, our method showed consistently better performance. ComPhy is a fast and robust tool for genome-wide inference of evolutionary
relationship among genomes.
doi:10.1186/1471-2105-10-S1-S5
2009-01-01T00:00:00ZEfficient Statistical Mapping of Avian Count DataRoyle, J. AndrewWikle, Christopher K., 1963-https://hdl.handle.net/10355/91192019-05-16T14:38:06Z2005-01-01T00:00:00ZEfficient Statistical Mapping of Avian Count Data
Royle, J. Andrew; Wikle, Christopher K., 1963-
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 spectral parameterization of the spatial process is very computationally efficient, enabling effective estimation and prediction in large problems using Markov chain Monte Carlo techniques. We apply this model to creating avian relative abundance maps from the North American Breeding Bird Survey (BBS) data. Variation in the ability of observers to count birds is modeled as spatially-independent noise, resulting in over-dispersion relative to the Poisson assumption. This approach represents an improvement over existing approaches used for spatial modeling of BBS data which are either inefficient for continental scale modeling and prediction or fail to accommodate important distributional features of count data thus leading to inaccurate accounting of prediction uncertainty.
This is the pre-print version of the article found in Environmental and Ecological Statistics. The original publication is available at www.springerlink.com.
2005-01-01T00:00:00Z