2003 Progress Report: Local Inferences from Aquatic Studies

EPA Grant Number: R829095C002
Subproject: this is subproject number 002 , established and managed by the Center Director under grant R829095
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).

Center: Space-Time Aquatic Resources Modeling and Analysis Program (STARMAP)
Center Director: Urquhart, N. Scott
Title: Local Inferences from Aquatic Studies
Investigators: Breidt, F. Jay , Davis, Richard A. , Hoeting, Jennifer A.
Current Investigators: Breidt, F. Jay , Davis, Richard A. , Hoeting, Jennifer A. , Opsomer, Jean
Institution: Colorado State University
Current Institution: Colorado State University , Iowa State University
EPA Project Officer: Packard, Benjamin H
Project Period: October 1, 2001 through September 30, 2006
Project Period Covered by this Report: October 1, 2002 through September 30, 2003
RFA: Research Program on Statistical Survey Design and Analysis for Aquatic Resources (2001) RFA Text |  Recipients Lists
Research Category: Aquatic Ecosystems , Ecological Indicators/Assessment/Restoration , Water and Watersheds , Water , Ecosystems

Objective:

The objective of this research project is to develop hierarchical spatio-temporal models for local inferences about aquatic resources.

Progress Summary:

Breidt and Ph.D. student Delorey are working on the broad topic of small area estimation, investigating methods for incorporating spatial relationships. The small areas of interest, for now, consist of watersheds (HUC8's) in the Mid-Atlantic Highlands Region, and the responses focus on characteristics of water quality. They currently are using Bayesian methods to construct a set of ensemble estimates for all watersheds and plan to investigate the same problem using non-parametric/semi-parametric methods.

Breidt and M.S. student Everson-Stewart have investigated nonparametric regression estimators for two-stage samples, in which auxiliary information is available at the level of the primary sampling units. This work used the Environmental Monitoring and Assessment Program Northeast Lakes data set. These nonparametric methods may be useful for estimation in regions with small, but not extremely small, sample sizes.

Breidt and colleague Jean Opsomer at Iowa State University are collaborating on semi-parametric model-assisted regression estimation as well as model-based estimation. These methods can be useful for small area estimation, and will be compared to fully parametric procedures. This project has made substantial progress in developing model-assisted estimates of distribution functions, primarily through the M.S. project of Alicia Johnson. (She completed her degree, and now is employed in an environmental position with the Center for Communicable Diseases.) The estimator compares favorably to other parametric and nonparametric alternatives. Results have been presented at several statistical meetings, and a paper on this is in preparation to be submitted shortly to Biometrika.

As an extension of the nonparametric model-assisted estimation project, Opsomer and Breidt have begun research in applying the nonparametric regression methodology to the small area estimation context and are applying it to the Northeastern Lakes survey. Preliminary results based on this work were presented at the joint meeting of the Space-Time Aquatic Resources Analysis Program (STARMAP) and Program on Designs and Models for Aquatic Resource Surveys(DAMARS) at Oregon State University in August. A simulation study was conducted by M.S. student Everson-Stewart, who has since completed her degree and taken a job with Amgen.

The choice of the smoothing parameter has an important effect on nonparametric regression estimators, and this also is true for model-assisted estimators. Opsomer and Miller, graduate students at Iowa State University, have been developing a cross validation-based method for selecting the smoothing parameter, and they presented preliminary results at the Joint Statistical Meetings in August 2003.

Future Activities:

Breidt, Opsomer, Delorey and postdoctoral fellow Giovanna Ranalli will continue to develop semiparametric methods for small area estimation, focusing on comparison penalize splines methods in a spatial context. These semiparametric procedures will be compared to fully parametric methods. A paper on this work, possibly using acid-neutralizing capacity data from the Modeling Mid-Atlantic Highland Streams Assessment, is planned.

Breidt and Opsomer are finalizing a manuscript on semiparametric additive regression estimation, which can accommodate models with multiple auxiliary variables of both continuous and categorical types, and can be a useful estimation procedure for regions with moderate sample sizes. Breidt, Opsomer, and M.S. student Everson-Stewart plan to complete and submit a paper on nonparametric regression estimation for two-stage spatial sampling. Applications to small area estimation will be considered.


Journal Articles on this Report : 6 Displayed | Download in RIS Format

Other subproject views: All 83 publications 20 publications in selected types All 18 journal articles
Other center views: All 302 publications 54 publications in selected types All 42 journal articles
Type Citation Sub Project Document Sources
Journal Article Andrews B, Davis RA, Breidt FJ. Maximum likelihood estimation for all-pass time series models. Journal of Multivariate Analysis 2006;97(7):1638-1659. R829095 (Final)
R829095C002 (2003)
R829095C002 (2004)
R829096 (2003)
  • Full-text: ScienceDirect-Full Text PDF
    Exit
  • Abstract: ScienceDirect-Abstract
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  • Journal Article Andrews B, Davis RA, Breidt FJ. Rank-based estimation for all-pass time series models. Annals of Statistics 2007;35(2):844-869. R829095 (2005)
    R829095 (Final)
    R829095C002 (2003)
    R829095C002 (2004)
  • Full-text: Project Euclid-Full Text PDF
    Exit
  • Abstract: Project Euclid-Abstract
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  • Other: Cornell University-Full Text PDF
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  • Journal Article Breidt FJ, Hsu N-J. Best mean square prediction for moving averages. Statistica Sinica 2005;15(2):427-446. R829095 (Final)
    R829095C002 (2003)
    R829095C002 (2004)
    R829095C002 (2005)
    R829096 (2003)
    R829096 (2005)
  • Full-text: StatisticaSinica-Full Text PDF
    Exit
  • Abstract: StatisticaSinica-Abstract
    Exit
  • Journal Article Breidt FJ, Claeskens G, Opsomer JD. Model-assisted estimation for complex surveys using penalised splines. Biometrika 2005;92(4):831-846. R829095 (Final)
    R829095C002 (2003)
    R829095C002 (2005)
  • Abstract: Oxford Journals-Abstract
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  • Other: Ku Leuven-Prepublication PDF
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  • Journal Article Breidt FJ, Opsomer JD, Johnson AA, Ranalli MG. Semiparametric model-assisted estimation for natural resource surveys. Survey Methodology 2007;33(1):35-44. R829095 (Final)
    R829095C002 (2003)
    R829095C002 (2004)
  • Full-text: Statistics Canada-Full Text PDF
    Exit
  • Abstract: Statistics Canada-TOC
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  • Journal Article Johnson AA, Breidt FJ, Opsomer JD. Estimating distribution functions from survey data using nonparametric regression. Journal of Statistical Theory and Practice 2008;2(3):419-431. R829095 (2004)
    R829095 (2005)
    R829095 (Final)
    R829095C002 (2003)
    R829095C002 (2005)
  • Abstract: Taylor&Francis-Abstract
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  • Other: Journal Issue Contents
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  • Supplemental Keywords:

    latent processes, Matern covariance function, model selection, remote sensing, sampling design, path analysis, kernel regression, thin plate splines, small area estimation, geographic information system, GIS, tessellation stratified sampling, water quality, land cover, land use, accuracy, precision, outreach, distance learning, web-based learning, needs-based instruction, accommodating cultural differences, management, efficiency,, RFA, Scientific Discipline, Ecosystem Protection/Environmental Exposure & Risk, Aquatic Ecosystems & Estuarine Research, Aquatic Ecosystem, Environmental Monitoring, EMAP, ecosystem monitoring, statistical survey design, spatial and temporal modeling, aquatic ecosystems, water quality, Environmental Monitoring and Assessment Program, modeling ecosystems, STARMAP

    Relevant Websites:

    http://www.stat.colostate.edu/starmap Exit

    Progress and Final Reports:

    Original Abstract
  • 2002
  • 2004 Progress Report
  • 2005 Progress Report
  • Final Report

  • Main Center Abstract and Reports:

    R829095    Space-Time Aquatic Resources Modeling and Analysis Program (STARMAP)

    Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
    R829095C001 Combining Environmental Data Sets
    R829095C002 Local Inferences from Aquatic Studies
    R829095C003 Development and Evaluation of Aquatic Indicators
    R829095C004 Extension of Expertise on Design and Analysis to States and Tribes
    R829095C005 Integration and Coordination for STARMAP