A computation saving Jackknife approach to receptor model uncertainty statements for serially correlated data
The use of receptor modeling is now a widely accepted approach to model air pollution data. The resulting estimates of pollution source profiles have error and frequently the uncertainties are obtained under an assumption of independence. In addition traditional Bootstrap approaches are very computa...
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| Published in: | Chemometrics and intelligent laboratory systems Vol. 88; no. 2; pp. 170 - 182 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
15-09-2007
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| Abstract | The use of receptor modeling is now a widely accepted approach to model air pollution data. The resulting estimates of pollution source profiles have error and frequently the uncertainties are obtained under an assumption of independence. In addition traditional Bootstrap approaches are very computationally intensive. We present an intuitive Jackknife alternative that is much less computationally intensive and in simulation examples and actual data seems to demonstrate that it provides wider confidence intervals and larger standard errors for receptor model profile estimates than does the Bootstrap done under the assumption of independence. |
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| AbstractList | The use of receptor modeling is now a widely accepted approach to model air pollution data. The resulting estimates of pollution source profiles have error and frequently the uncertainties are obtained under an assumption of independence. In addition traditional Bootstrap approaches are very computationally intensive. We present an intuitive Jackknife alternative that is much less computationally intensive and in simulation examples and actual data seems to demonstrate that it provides wider confidence intervals and larger standard errors for receptor model profile estimates than does the Bootstrap done under the assumption of independence. |
| Author | Spiegelman, Clifford H. Park, Eun Sug |
| Author_xml | – sequence: 1 givenname: Clifford H. surname: Spiegelman fullname: Spiegelman, Clifford H. email: chemometrics@aol.com organization: Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843-3143, USA – sequence: 2 givenname: Eun Sug surname: Park fullname: Park, Eun Sug organization: Texas Transportation Institute, Texas A&M University System, 3135 TAMU, College Station, TX 77843-3135, USA |
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| CitedBy_id | crossref_primary_10_1080_00401706_2017_1366948 crossref_primary_10_1002_env_1021 crossref_primary_10_1016_j_chemolab_2020_104166 crossref_primary_10_1080_00401706_2013_765321 |
| Cites_doi | 10.1016/S0169-7439(01)00200-3 10.1016/S0169-7439(99)00015-5 10.1016/j.chemolab.2005.04.001 10.1073/pnas.94.13.6596 10.1002/env.557 10.1002/env.654 10.1198/016214501753381823 10.1198/004017002188618527 |
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| Keywords | Bootstrap Jackknife Bilinear Air-pollution |
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| References | Henry, Park, Spiegelman (bib8) 1999; 48 Henry, Spiegelman, Collins, Park (bib1) 1997; 94 Park, Spiegelman, Henry (bib4) 2002; 13 Christensen, Sain (bib5) 2002; 44 B.J. Gajewski, PhD dissertation, 2000. Paatero, Hopke, Song, Ramadan (bib3) 2002; 60 Park, Henry, Spiegelman (bib9) 2000; 29 Park, Guttorp, Henry (bib2) 2001; 96 Gajewski, Spiegelman (bib6) 2004; 15 Henry (bib7) 2005; 77 Efron, Tibshirani (bib12) 1993 Tukey, Mosteller (bib10) 1977 Park (10.1016/j.chemolab.2007.04.004_bib4) 2002; 13 Christensen (10.1016/j.chemolab.2007.04.004_bib5) 2002; 44 Park (10.1016/j.chemolab.2007.04.004_bib2) 2001; 96 Park (10.1016/j.chemolab.2007.04.004_bib9) 2000; 29 10.1016/j.chemolab.2007.04.004_bib11 Henry (10.1016/j.chemolab.2007.04.004_bib1) 1997; 94 Gajewski (10.1016/j.chemolab.2007.04.004_bib6) 2004; 15 Henry (10.1016/j.chemolab.2007.04.004_bib8) 1999; 48 Tukey (10.1016/j.chemolab.2007.04.004_bib10) 1977 Efron (10.1016/j.chemolab.2007.04.004_bib12) 1993 Henry (10.1016/j.chemolab.2007.04.004_bib7) 2005; 77 Paatero (10.1016/j.chemolab.2007.04.004_bib3) 2002; 60 |
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| SubjectTerms | Air-pollution Bilinear Bootstrap Jackknife |
| Title | A computation saving Jackknife approach to receptor model uncertainty statements for serially correlated data |
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