2005 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. , Opsomer, Jean
Institution: Colorado State University , Iowa State University
EPA Project Officer: Hiscock, Michael
Project Period: October 1, 2001 through September 30, 2006
Project Period Covered by this Report: October 1, 2004 through September 30, 2005
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 inferencesabout aquatic resources.

Progress Summary:

Work continues in the use of nonparametric modeling for survey regression estimation, jointly supported by Designs and Models for Aquatic Resource Surveys and Space-Time Aquatic Resources Modeling and Analysis Program (STARMAP). Numerous invited talks have been given and several manuscripts have been submitted and published. The problems of interest in this context combine landscape-level auxiliary data (such as those from GIS coverages) with field observations. The inferential problems range from model-assisted descriptive inferences for aquatic populations to model-based small area estimates. Breidt and Opsomer, together with a postdoctoral fellow, students, and colleagues, extended earlier results on local polynomial survey regression estimation in a number of directions, some of which are described here. The M.S. project of Alicia Johnson on cumulative distribution function (CDF) estimation was submitted for publication, and the M.S. project of Siobhan Everson-Stewart on two-dimensional kernel estimators was extended by Giovanna Ranalli, the postdoctoral research fellow, to the setting of penalized splines. Ranalli and Breidt also obtained promising preliminary results on the use of low rank radial basis functions for smoothing data from river networks.

Ji-Yeon Kim successfully defended her doctoral degree at Iowa State University under the direction of Breidt and Opsomer and submitted a joint paper on two-stage local polynomial regression estimation. Doctoral student Jehad Al-Jararha is adapting the two-stage local polynomial estimator to other cluster-mean models and other types of auxiliary information. New work was conducted with Gerda Claeskens on nonparametric model-assisted estimation using penalized splines and was extended to the case of small area estimation. Doctoral student Mark Delorey is extending further penalized spline estimators to the setting of two-stage sampling.

Nan-Jung Hsu and Hsin-Cheng Huang from Taiwan visited Colorado State University (CSU) as research scientists collaborating with Breidt and other STARMAP researchers during the period of August through November 2004. Breidt and Hsu continued work on semiparametric modeling for increment-averaged data, such as a rise in soil and sediment core sampling. Breidt began to extend these methods to allow for semiparametric small area estimation of profiles, which are infinite-dimensional parameters. Doctoral student Bill Coar collaborated with Hsu and Breidt on autocorrelation diagnostics for increment data, using Cholesky autocovariance models. Coar and Breidt have been using similar techniques to develop a class of state-space models for stream networks, with corresponding Kalman-like filtering and fixed-network smoothing algorithms, and likelihood-based estimation techniques. Hsu and Breidt are working on Bayesian estimation for non-Gaussian noninvertible moving average models, with potential application to spatial data from river networks. Hsin-Cheng Huang worked on improving the power for trend detection of lake water quality using spatio-temporal models. Hsu, Breidt, and Huang are developing a new variable selection method for multiple layers of GIS data using Lasso and have collaborated with Theobald (see report for Grant No. R829095C003) in its implementation. Such a methodology would be applicable immediately to aquatic resource data, for which multiple layers of geospatial data are available as potential regressors.

Future Activities:

Planned developments include: (1) completing theory and practical methods for the two-stage sampling case (splines and kernels); (2) developing a new theory for semiparametric small area estimation; (3) completing further work on uncertainty estimates for the spatial lasso model selection procedure; (4) estimating maximum likelihood via the EM algorithm for network autoregressive models; and (5) determining methods for likelihood estimation and efficient Bayesian inference in network state-space models.

Anticipated Research Outputs

  • Research results concerning analysis methodology for use in analyzing aquatic monitoring data, published in both statistical and monitoring outlets.
  • Methodology and tools for predicting the probability that a point on a stream will be perennial or nonperennial. (This will be added to the sampling methodology described above.)


Journal Articles on this Report : 14 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 Breidt FJ, Hsu N-J. Best mean square prediction for moving averages. Statistica Sinica 2005;15(2):427-446. R829095 (Final)
R829095C002 (2003)
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  • Full-text: StatisticaSinica-Full Text PDF
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  • Abstract: StatisticaSinica-Abstract
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  • 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 Da Silva DN, Opsomer JD. Properties of the weighting cell estimator under a nonparametric response mechanism. Survey Methodology 2004;30(1):45-55. R829095 (2003)
    R829095 (2004)
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  • Full-text: Statistics Canada-Full Text PDF
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  • Abstract: Statistics Canada-Abstract
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  • Journal Article Francisco-Fernandez M, Opsomer JD. Smoothing parameter selection methods for nonparametric regression with spatially correlated errors. Canadian Journal of Statistics 2005;33(2):279-295. R829095 (Final)
    R829095C002 (2004)
    R829095C002 (2005)
  • Full-text: Universidade da Coruna-Preprint PDF
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  • Abstract: Wiley-Abstract
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  • Journal Article Francisco-Fernandez M, Jurado-Exposito M, Opsomer JD, Lopez-Granados F. A nonparametric analysis of the spatial distribution of Convolvulus arvensis in wheat-sunflower rotations. Environmetrics 2006;17(8):849-860. R829095 (2004)
    R829095 (2005)
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  • Full-text: Universidade da Coruna-Prepublication PDF
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  • Abstract: Wiley Interscience Abstract
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  • Journal Article Hall P, Opsomer JD. Theory for penalised spline regression. Biometrika 2005;92(1):105-118. R829095 (Final)
    R829095C002 (2005)
  • Abstract: Biometrika
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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)
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    R829095 (Final)
    R829095C002 (2003)
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  • Abstract: Taylor&Francis-Abstract
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  • Other: Journal Issue Contents
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  • Journal Article Kauermann G, Opsomer JD. Generalized cross-validation for bandwidth selection of backfitting estimates in generalized additive models. Journal of Computational & Graphical Statistics 2004;13(1):66-89. R829095 (Final)
    R829095C002 (2005)
  • Full-text: Research Gate-Abstract and Full Text HTML
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  • Abstract: Taylor & Francis - Abstract
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  • Journal Article Montanari GE, Ranalli MG. Nonparametric model calibration estimation in survey sampling. Journal of the American Statistical Association 2005;100(472):1429-1442. R829095 (Final)
    R829095C002 (2004)
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    R829096 (2004)
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  • Abstract: American Statistical Association-Abstract
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  • Journal Article Opsomer JD, Miller CP. Selecting the amount of smoothing in nonparametric regression estimation for complex surveys. Journal of Nonparametric Statistics 2005;17(5):593-611. R829095 (Final)
    R829095C002 (2004)
    R829095C002 (2005)
  • Abstract: Taylor & Francis-Abstract
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  • Journal Article Opsomer JD, Breidt FJ, Moisen GG, Kauermann G. Model-assisted estimation of forest resources with generalized additive models. Journal of the American Statistical Association 2007;102(478):400-409. R829095 (2004)
    R829095 (2005)
    R829095 (Final)
    R829095C002 (2004)
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  • Full-text: US Forest Service-Full Text PDF
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  • Abstract: Journal of the American Statistical Association-Abstract
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  • Journal Article Opsomer JD, Claeskens G, Ranalli MG, Kauermann G, Breidt FJ. Non-parametric small area estimation using penalized spline regression. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2008;70(1):265-286. R829095C002 (2005)
    R829096 (2005)
  • Abstract: Wiley
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  • Other: Prepublication PDF
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  • Journal Article Opsomer JD, Francisco-Fernandez M. Finding local departures from a parametric model using nonparametric regression. Statistical Papers 2010;51(1):69-84. R829095C002 (2005)
  • Abstract: SpringerLink-Abstract
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  • Journal Article Wang H, Ranalli MG. Low-rank smoothing splines on complicated domains. Biometrics 2007;63(1):209-217. R829095 (2004)
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    R829095 (Final)
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  • Abstract from PubMed
  • Abstract: Wiley Interscience-Abstract
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  • Other: BNU-PrepublicationFull Text PDF
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  • Supplemental Keywords:

    kernel regression, thin plate splines,, 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
  • 2003 Progress Report
  • 2004 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