15 results match your criteria Environmental and Ecological Statistics [Journal]

  • Page 1 of 1

A Bayesian mixture model for missing data in marine mammal growth analysis.

Environ Ecol Stat 2016 Dec 4;23(4):585-603. Epub 2016 Oct 4.

Department of Statistics, Florida State University, Tallahassee, FL 32306, USA.

Much of what is known about bottle nose dolphin () anatomy and physiology is based on necropsies from stranding events. Measurements of total body length, total body mass, and age are used to estimate growth. It is more feasible to retrieve and transport smaller animals for total body mass measurement than larger animals, introducing a systematic bias in sampling. Read More

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http://dx.doi.org/10.1007/s10651-016-0355-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5425172PMC
December 2016
13 Reads

A model-based approach for imputing censored data in source apportionment studies.

Environ Ecol Stat 2015 Dec 4;22(4):779-800. Epub 2015 Jun 4.

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD 21205, Tel.: 410-955-2468.

Sources of particulate matter (PM) air pollution are generally inferred from PM chemical constituent concentrations using source apportionment models. Concentrations of PM constituents are often censored below minimum detection limits (MDL) and most source apportionment models cannot handle these censored data. Frequently, censored data are first substituted by a constant proportion of the MDL or are removed to create a truncated dataset before sources are estimated. Read More

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http://dx.doi.org/10.1007/s10651-015-0319-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4667983PMC
December 2015
12 Reads

Resampling-based multiple comparison procedure with application to point-wise testing with functional data.

Environ Ecol Stat 2015 Mar 22;22(1):45-59. Epub 2014 Apr 22.

National Institute of Environmental Health Sciences, National Institutes of Health, USA.

In this paper we describe a coherent multiple testing procedure for correlated test statistics such as are encountered in functional linear models. The procedure makes use of two different -value combination methods: the Fisher combination method and the Šidák correction-based method. -values for Fisher's and Šidák's test statistics are estimated through resampling to cope with the correlated tests. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5040358PMC
http://dx.doi.org/10.1007/s10651-014-0282-7DOI Listing
March 2015
14 Reads

A Flexible Spatio-Temporal Model for Air Pollution with Spatial and Spatio-Temporal Covariates.

Environ Ecol Stat 2014 Sep;21(3):411-433

University of Washington, Seattle, USA.

The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system (GIS) covariates. The model presented in this paper has been implemented in an R package, SpatioTemporal, available on CRAN. Read More

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http://dx.doi.org/10.1007/s10651-013-0261-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4174563PMC
September 2014
11 Reads

Spatiotemporal modeling of irregularly spaced Aerosol Optical Depth data.

Environ Ecol Stat 2013 Jun;20(2):297-314

Department of Biostatistics, The University of Iowa, Iowa City, IA, USA.

Many advancements have been introduced to tackle spatial and temporal structures in data. When the spatial and/or temporal domains are relatively large, assumptions must be made to account for the sheer size of the data. The large data size, coupled with realities that come with observational data, make it difficult for all of these assumptions to be met. Read More

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http://dx.doi.org/10.1007/s10651-012-0221-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3901316PMC
June 2013
4 Reads

Space-time stick-breaking processes for small area disease cluster estimation.

Environ Ecol Stat 2013 Mar;20(1):91-107

Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.

We propose a space-time stick-breaking process for the disease cluster estimation. The dependencies for spatial and temporal effects are introduced by using space-time covariate dependent kernel stick-breaking processes. We compared this model with the space-time standard random effect model by checking each model's ability in terms of cluster detection of various shapes and sizes. Read More

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http://dx.doi.org/10.1007/s10651-012-0209-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3712540PMC
March 2013
9 Reads

Infant Mortality and Social Environment in Georgia: An application of hotspot detection and prioritization.

Environ Ecol Stat 2010 Dec;17(4):455-471

The Social Science Research Institute, the Pennsylvania State University, USA. 803 Oswald Tower, University Park, PA 16802. Tel: 814-865-5553.

Recent years have witnessed the growth of new information technologies and their applications to various disciplines. The goal of this paper is to demonstrate how the two innovative methods, upper level set scan (ULS) hotspot detection and the multicriterion prioritization scheme, facilitate population health and break new ground in public health surveillance. It is believed that the social environment (i. Read More

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http://link.springer.com/10.1007/s10651-010-0166-4
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http://dx.doi.org/10.1007/s10651-010-0166-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3197227PMC
December 2010
8 Reads

Space-time Bayesian small area disease risk models: development and evaluation with a focus on cluster detection.

Environ Ecol Stat 2010 Mar;17(1):73-95

Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, 800 Sumter Street, Columbia, SC 29208, USA.

This paper extends the spatial local-likelihood model and the spatial mixture model to the space-time (ST) domain. For comparison, a standard random effect space-time (SREST) model is examined to allow evaluation of each model's ability in relation to cluster detection. To pursue this evaluation, we use the ST counterparts of spatial cluster detection diagnostics. Read More

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http://link.springer.com/10.1007/s10651-008-0102-z
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http://dx.doi.org/10.1007/s10651-008-0102-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2953810PMC
March 2010
10 Reads

Power Evaluation of Focused Cluster Tests.

Environ Ecol Stat 2010 Sep;17(3):303-316

Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, USA.

Many statistical tests have been developed to assess the significance of clusters of disease located around known sources of environmental contaminants, also known as focused disease clusters. The majority of focused-cluster tests were designed to detect a particular spatial pattern of clustering, one in which the disease cluster centers around the pollution source and declines in a radial fashion with distance. However, other spatial patterns of environmentally related disease clusters are likely given that the spatial dispersion patterns of environmental contaminants, and thus human exposure, depend on a number of factors (i. Read More

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http://dx.doi.org/10.1007/s10651-009-0108-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4033302PMC
September 2010
6 Reads

Local multiplicity adjustments for spatial cluster detection.

Authors:
Ronald E Gangnon

Environ Ecol Stat 2010 ;17(1):55-71

Departments of Biostatistics and Medical Informatics and Population Health Sciences, 603 WARF Office Building, University of Wisconsin-Madison, 610 Walnut Street, Madison, WI 53726, USA,

The spatial scan statistic is a widely applied tool for cluster detection. The spatial scan statistic evaluates the significance of a series of potential circular clusters using Monte Carlo simulation to account for the multiplicity of comparisons. In most settings, the extent of the multiplicity problem varies across the study region. Read More

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http://dx.doi.org/10.1007/s10651-008-0101-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2871332PMC
January 2010
4 Reads

Bootstrap methods for simultaneous benchmark analysis with quantal response data.

Environ Ecol Stat 2009 Mar;16(1):63-73

Department of Statistics, Texas A&M University, College Station, TX 77843, USA.

A primary objective in quantitative risk assessment is the characterization of risk which is defined to be the likelihood of an adverse effect caused by an environmental toxin or chemcial agent. In modern risk-benchmark analysis, attention centers on the "benchmark dose" at which a fixed benchmark level of risk is achieved, with a lower confidence limits on this dose being of primary interest. In practice, a range of benchmark risks may be under study, so that the individual lower confidence limits on benchmark dose must be corrected for simultaneity in order to maintain a specified overall level of confidence. Read More

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http://dx.doi.org/10.1007/s10651-007-0073-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2659674PMC
March 2009
6 Reads

Confidence limits on one-stage model parameters in benchmark risk assessment.

Environ Ecol Stat 2009 Mar;16(1):53-62

Department of Mathematics, Northern Kentucky University, Highland Heights, KY 41099, USA.

In modern environmental risk analysis, inferences are often desired on those low dose levels at which a fixed benchmark risk is achieved. In this paper, we study the use of confidence limits on parameters from a simple one-stage model of risk historically popular in benchmark analysis with quantal data. Based on these confidence bounds, we present methods for deriving upper confidence limits on extra risk and lower bounds on the benchmark dose. Read More

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http://dx.doi.org/10.1007/s10651-007-0076-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2659669PMC
March 2009
8 Reads

Statistics in disease ecology: introduction to a special issue.

Authors:
Lance A Waller

Environ Ecol Stat 2008 ;15(3):259-263

Department of Biostatistics, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA e-mail:

The three papers included in this special issue represent a set of presentations in an invited session on disease ecology at the 2005 Spring Meeting of the Eastern North American Region of the International Biometric Society. The papers each address statistical estimation and inference for particular components of different disease processes and, taken together, illustrate the breadth of statistical issues arising in the study of the ecology and public health impact of disease. As an introduction, we provide a very brief overview of the area of "disease ecology", a variety of synonyms addressing different aspects of disease ecology, and present a schematic structure illustrating general components of the underlying disease process, data collection issues, and different disciplinary perspectives ranging from microbiology to public health surveillance. Read More

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http://dx.doi.org/10.1007/s10651-007-0058-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2600543PMC
January 2008
4 Reads

Accounting for rate instability and spatial patterns in the boundary analysis of cancer mortality maps.

Authors:
Pierre Goovaerts

Environ Ecol Stat 2008 Dec;15(4):421-446

BioMedware, Inc., E-mail:

Boundary analysis of cancer maps may highlight areas where causative exposures change through geographic space, the presence of local populations with distinct cancer incidences, or the impact of different cancer control methods. Too often, such analysis ignores the spatial pattern of incidence or mortality rates and overlooks the fact that rates computed from sparsely populated geographic entities can be very unreliable. This paper proposes a new methodology that accounts for the uncertainty and spatial correlation of rate data in the detection of significant edges between adjacent entities or polygons. Read More

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http://dx.doi.org/10.1007/s10651-007-0064-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2136438PMC
December 2008
7 Reads

Boundaries, links and clusters: a new paradigm in spatial analysis?

Environ Ecol Stat 2008 Dec;15(4):403-419

BioMedware, 516 North State Street, Ann Arbor, MI 48104-1236, USA.

This paper develops and applies new techniques for the simultaneous detection of boundaries and clusters within a probabilistic framework. The new statistic "little b" (written b(ij)) evaluates boundaries between adjacent areas with different values, as well as links between adjacent areas with similar values. Clusters of high values (hotspots) and low values (coldspots) are then constructed by joining areas abutting locations that are significantly high (e. Read More

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http://dx.doi.org/10.1007/s10651-007-0066-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2435220PMC
December 2008
4 Reads
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