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Exploring Dependence with Data on Spatial Lattices
by
Kaiser, Mark S.
, Caragea, Petruţa C.
in
Automodels
/ Biometric Methodology
/ biometry
/ Biometry - methods
/ Biostatistics
/ Computer Simulation
/ Conditionally specified models
/ Data Interpretation, Statistical
/ Datasets
/ Estimators
/ Exploratory analysis
/ Markov analysis
/ Markov Chains
/ Markov models
/ Mathematical independent variables
/ Modeling
/ Models, Biological
/ Models, Statistical
/ Neighborhoods
/ Parametric models
/ Preliminary estimates
/ Simulation
/ spatial data
/ Spatial models
/ Spatial structure
/ Statistical dependence
/ Statistical median
2009
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Exploring Dependence with Data on Spatial Lattices
by
Kaiser, Mark S.
, Caragea, Petruţa C.
in
Automodels
/ Biometric Methodology
/ biometry
/ Biometry - methods
/ Biostatistics
/ Computer Simulation
/ Conditionally specified models
/ Data Interpretation, Statistical
/ Datasets
/ Estimators
/ Exploratory analysis
/ Markov analysis
/ Markov Chains
/ Markov models
/ Mathematical independent variables
/ Modeling
/ Models, Biological
/ Models, Statistical
/ Neighborhoods
/ Parametric models
/ Preliminary estimates
/ Simulation
/ spatial data
/ Spatial models
/ Spatial structure
/ Statistical dependence
/ Statistical median
2009
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Do you wish to request the book?
Exploring Dependence with Data on Spatial Lattices
by
Kaiser, Mark S.
, Caragea, Petruţa C.
in
Automodels
/ Biometric Methodology
/ biometry
/ Biometry - methods
/ Biostatistics
/ Computer Simulation
/ Conditionally specified models
/ Data Interpretation, Statistical
/ Datasets
/ Estimators
/ Exploratory analysis
/ Markov analysis
/ Markov Chains
/ Markov models
/ Mathematical independent variables
/ Modeling
/ Models, Biological
/ Models, Statistical
/ Neighborhoods
/ Parametric models
/ Preliminary estimates
/ Simulation
/ spatial data
/ Spatial models
/ Spatial structure
/ Statistical dependence
/ Statistical median
2009
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Journal Article
Exploring Dependence with Data on Spatial Lattices
2009
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Overview
The application of Markov random field models to problems involving spatial data on lattice systems requires decisions regarding a number of important aspects of model structure. Existing exploratory techniques appropriate for spatial data do not provide direct guidance to an investigator about these decisions. We introduce an exploratory quantity that is directly tied to the structure of Markov random field models based on one-parameter exponential family conditional distributions. This exploratory diagnostic is shown to be a meaningful statistic that can inform decisions involved in modeling spatial structure with statistical dependence terms. In this article, we develop the diagnostic, illustrate its use in guiding modeling decisions with simulated examples, and reexamine a previously published application.
Publisher
Blackwell Publishing Inc,Wiley-Blackwell,Blackwell Publishing Ltd
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