Multivariate receptor modeling from a statistical science viewpoint /
Receptor modeling is a collection of methods used to model
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| Format: | Thesis Book |
| Language: | English |
| Published: |
[Place of publication not identified] :
[publisher not identified] ;
1997.
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| Subjects: | |
| Online Access: | http://proxy.library.tamu.edu/login?url=http://proquest.umi.com/pqdweb?did=736824721&sid=1&Fmt=2&clientId=2945&RQT=309&VName=PQD |
| Summary: | Receptor modeling is a collection of methods used to model the air pollution data. Although the main interest is identifying pollution sources, the determination of the number of major sources is the first problem that we should overcome. The NUMFACT algorithm is a new method of determining the number of underlying factors in a multivariate system. Although NUMFACT has not been previously described, it has been used in high profile studies. The asymptotic distribution of NUMFACT statistics and their associated cutoff points are derived. Modified NUMFACT statistics and related statistics, W, are also presented with a new decision rule for determining the number of sources. The simulation study shows that for the lognormal error case, they are superior to the traditional methods such as Bartlett's statistic or the rule of thumb methods, and for the normal error case, they are competitive with the best of the traditional methods. Many air pollution datasets typically consist of the measurements on fifty or sixty variables, and it is often too large to handle all at once. We develop two new algorithms, SPECIESA and SPECIESB, for choosing the species used in final model fitting. The simulation results show that these algorithms work very well in choosing the species generated by the sources not by the measurement errors. Estimating the source profiles and their contributions are our primary concerns in receptor modeling. We take the constrained nonlinear least squares approach to provide those estimates under as little assumptions as possible about the model parameters. The assumptions that we make are necessary to get the model identifiability. A set of algorithms, VERTEX, to find the least squares solution under different versions of the model is introduced. The resulting estimators are shown to be consistent and asymptotically normal under appropriate identifiability conditions. The proposed methods are applied to simulated data and real air pollution data. |
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| Item Description: | Vita. "Major Subject: Statistics". |
| Physical Description: | xi, 153 leaves : illustrations ; 28 cm. Issued also on microfiche from University Microfilms Inc. |
| Bibliography: | Includes bibliographical references: pages 143-147. |