Permutation/randomization-based inference for environmental data
详细信息 查看全文
- 作者：R. Christopher Spicer ; Harry J. Gangloff
- 关键词：Randomization ; Permutation ; Inference ; Distribution ; Detection frequency
- 刊名：Environmental Monitoring and Assessment
- 出版年：2016
- 出版时间：March 2016
- 年：2016
- 卷：188
- 期：3
- 全文大小：974 KB
- 参考文献：Agresti, A. (2001). Exact inference for categorical data: recent advances and continuing controversies. Statistics in Medicine, 20, 2709–2722.CrossRef
ASTM International. (2007). ASTM D 7390–07. Standard guide for evaluating asbestos in dust on surfaces by comparison between two environments. Conshohocken PA: ASTM International West.
Biau, D. J., Jolles, B. M., & Porcher, R. (2010). P value and the theory of hypothesis testing: an explanation for new researchers. Clinical Orthopaedics and Related Research, 468, 885–892.CrossRef
Burge, H. J., Macher, J., & Milton, D. (1999). Data Evaluation. In J. Macher (Ed.), Bioaerosols: assessment and control (pp. 14–1). Cincinnati (OH): American Conference of Governmental Industrial Hygienists (ACGIH).
Chernoff, H. (2002). A view of past present and future. The Indian Journal of Statistics San Antonio Conference selected articles, 64(A2), 183–194.
David, H. A. (2008). The beginnings of randomization tests. American Statistician, 62, 70–72.CrossRef
DiNocera, F., & Ferlazzo, F. (2000). Resampling approach to statistical inference: bootstrapping from event-related potentials data. Behavior Research Methods, Instruments, & Computers, 32, 111–119.CrossRef
Eden, T., & Yates, F. (1933). On the validity of Fishers’ z test when applied to an actual example of non-normal data. Journal of Agricultural Science, i, XXIII.
Eduard, W. (2009). Fungal spores: a critical review of the toxicological and epidemiological evidence as a basis for occupational exposure limit setting. Critical Reviews in Toxicology, 39, 799–864.CrossRef
Efron, B., & Tibshirani, R. (1986). Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statistical Science, 1, 54–77.CrossRef
Efron, B., & Tibshirani, R. (1998a). Permutation tests. In B. Efron & R. Tibshirani (Eds.), An introduction to the bootstrap (pp. 202–219). New York, NY: Chapman and Hall/ CRC.
Efron, B., & Tibshirani, R. (1998b). Hypothesis testing with the bootstrap. In B. Efron & R. Tibshirani (Eds.), An introduction to the bootstrap (pp. 220–236). New York, NY: Chapman and Hall/CRC.
Ernst, M. D. (2004). Permutation methods: a basis for exact inference. Statistical Science, 19, 676–685.CrossRef
Fisher, R. A. (1990a). The design of experiments. In Statistical methods experimental design and scientific inference (8th ed.). New York, NY: Oxford University Press.
Fisher, R. A. (1990b). Statistical methods and scientific inference. In Statistical methods experimental design and scientific inference (3rd ed.). New York, NY: Oxford University Press.
Gliner, J. A., Leech, N. L., & Morgan, G. A. (2002). Problems with null hypothesis significance testing (NHST): what do the textbooks say? Journal of Experimental Education, 71, 83–92.CrossRef
Hung, L. L., Miller, J. D., & Dillon, H. K. (2005). Viable fungi and bacteria in air, bulk, and surface samples and Legionella. In Guide for the determination of biological contaminants in environmental samples (2nd ed.). Fairfax (VA): American Industrial Hygiene Association.CrossRef
Hurlbert, S. H., & Lombardi, C. M. (2009). Final collapse of the Neyman-Pearson decision theoretic framework and rise of the neoFisherian. Annales Zoologici Fennici, 46, 311–349.CrossRef
Institute of the National Academies. (2004). Exposure assessment. In Damp indoor spaces and health (pp. 90–124). Washington, DC: National Academies Press.
Johnson, D., Thompson, D., Clinkenbeard, R., & Redus, J. (2008). Professional judgment and interpretation of viable mold air sampling data. Journal of Occupational and Environmental Hygiene, 5, 656–663.CrossRef
Kass, R. E. (2011). Statistical inference: the big picture. Statistical Science, 26, 1–9.CrossRef
Larrañaga, M. D., Karunasena, E., Holder, H. W., Althouse, E. D., & Straus, D. C. (2011). Statistical considerations for bioaerosol health-risk exposure analysis. In N. Mazzeo (Ed.), Chemistry, emission control, radioactive pollution and indoor air quality (pp. 272–310). Rijeka, Croatia: Intech Europe.
Lehmann, E. L. (1993). The Fisher, Neyman-Pearson theories of testing hypotheses: one theory or two? Journal of the American Statistical Association, 88, 1242–1249.CrossRef
Ludbrook, J., & Dudley, H. (1998). Why permutation tests are superior to t and F tests in biomedical research. American Statistician, 52, 127–132.
Modarres, R., Gastwirth, J. L., & Ewens, W. (2005). A cautionary note on the use of non-parametric tests in the analysis of environmental data. Environmetrics, 16, 319–326.CrossRef
New York State Department of Health. (2010). New York State Toxic Mold Task Force Final Report, December 2010. Albany, NY: New York State Department of Health.
Pesarin, F., & Salmaso, L. (2010). The permutation testing approach: a review. Statistica, anno LXX, 4, 481–509.
Prezant, B., Weekes, D. M., & Miller, J. D. (2008). Sampling design strategy. In Recognition, evaluation, and control of indoor mold (pp. 129–151). Fairfax (VA): American Industrial Hygiene Association.
Spicer, R. C., & Gangloff, H. J. (2000). Limitations in application of Spearman’s rank correlation to bioaerosol sampling data. American Industrial Hygiene Journal, 61, 362–366.CrossRef
Spicer, R. C., & Gangloff, H. J. (2003). Bioaerosol data distribution: probability and implications for sampling in evaluating problematic buildings. Applied Industrial Hygiene, 18, 584–590.
Spicer, R. C., & Gangloff, H. J. (2008). Verifying interpretive criteria for bioaerosol data using (bootstrap) Monte Carlo techniques. Journal of Occupational and Environmental Hygiene, 5, 85–92.CrossRef
Spicer, R. C., & Gangloff, H. J. (2010). Differences in detection frequency as a bioaerosol data criterion for evaluating suspect fungal contamination. Building and Environment, 45, 1304–131.CrossRef
Spicer, R. C., & Gangloff, H. J. (2011). Implications of error rates associated with numerical criteria for airborne fungal data. In Proceedings of Indoor Air 2011 (12th ed., pp. 398–404). Austin, TX: International Conference on Indoor Air Quality and Climate.
Spicer, R. C., & Gangloff, H. J. (2015). The building performance model for evaluating bioaerosol data from suspect indoor environments. Indoor and Built Environment, 24, 640–649.CrossRef
Sterne, J. A. C. (2002). Teaching hypothesis tests—time for a significant change? Statistics in Medicine, 21, 985–994.CrossRef
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: heuristics and biases. Science, 185, 1124–113.CrossRef
United States Environmental Protection Agency (USEPA). (1992). Statistical methods for evaluating the attainment of cleanup standards. Volume 3: Reference-based standards for soils and solid media, EPA 230-R-94-004. Washington, DC: USEPA.
United States Environmental Protection Agency (USEPA). (2003). World Trade Center background study report interim final. New York, New York: USEPA.
United States Environmental Protection Agency (USEPA). (2008). Lower Manhattan test and clean report. New York, NY: USEPA.
Yates, F. (1990). Forward to statistical methods experimental design and scientific inference (pp. vii–xxxii). New York, New York: Oxford University Press. - 作者单位：R. Christopher Spicer (1)
Harry J. Gangloff (1)
1. WCD Group LLC, 23 Route 31 North, Pennington, NJ, 08534, USA - 刊物类别：Earth and Environmental Science
- 刊物主题：Environment
Monitoring, Environmental Analysis and Environmental Ecotoxicology
Ecology
Atmospheric Protection, Air Quality Control and Air Pollution
Environmental Management - 出版者：Springer Netherlands
- ISSN：1573-2959
文摘
Quantitative inference from environmental contaminant data is almost exclusively from within the classic Neyman/Pearson (N/P) hypothesis-testing model, by which the mean serves as the fundamental quantitative measure, but which is constrained by random sampling and the assumption of normality in the data. Permutation/randomization-based inference originally forwarded by R. A. Fisher derives probability directly from the proportion of the occurrences of interest and is not dependent upon the distribution of data or random sampling. Foundationally, the underlying logic and the interpretation of the significance of the two models vary, but inference using either model can often be successfully applied. However, data examples from airborne environmental fungi (mold), asbestos in settled dust, and 1,2,3,4-tetrachlorobenzene (TeCB) in soil demonstrate potentially misleading inference using traditional N/P hypothesis testing based upon means/variance compared to permutation/randomization inference using differences in frequency of detection (Δf d). Bootstrapping and permutation testing, which are extensions of permutation/randomization, confirm calculated p values via Δf d and should be utilized to verify the appropriateness of a given data analysis by either model. Keywords Randomization Permutation Inference Distribution Detection frequency