Compositional Receptor Modeling

  title={Compositional Receptor Modeling},
  author={Dean Billheimer},
Receptor models apportion an ambient mixture of pollutants to the contributing pollution sources. Often, neither the number of sources nor their chemical profiles are known precisely. The dual goals of modeling are to estimate the chemical “signature” of the sources, and to characterize the mixing process. I develop a novel modeling approach for receptor data where all model components are compositions (i.e., vectors of proportions). This approach maintains positivity and summation constraints… CONTINUE READING
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Publications referenced by this paper.
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Statistical Interpretation of Species Composition.

D. Billheimer, W. F. Fagan, P. Guttorp
View 4 Excerpts
Highly Influenced

The Statistical Analysis of Compositional Data

J. Aitchison
View 5 Excerpts
Highly Influenced

Multivariate Receptor Modeling for Temporally Correlated Data by Using MCMC.

E. S. Park, P. Guttorp, R. C. Henry
National Research Center for Statistics and the Environment, • 2000

An Introduction to Source Receptor Modeling.

P. K. Hopke
View 1 Excerpt

Resolution of Additive Mixtures into Source Components and Contributions: A Compositional Approach.

K. Bandeen-Roche
J. Amer. Statist. Assoc., • 1994

Receptor Models for Air Quality Management

P. K. Hopke
View 1 Excerpt

Multidimensional multivariate Gaussian Markov random fields with applications to image processing.

K. V. Mardia
J. Multivariate Anal. 24, • 1988

The Geometry of Mixture Likelihoods, I: General Theory.

B. G. Lindsay
Annals of Statist., • 1983
View 1 Excerpt

Consistency of the Maximum Likelihood Estimator in the Presence of Infinitely Many Incidental Parameters.

J. Kiefer, J. Wolfowitz
Ann. Math. Statist., • 1956
View 2 Excerpts

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