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Comparison of data analysis procedures for real-time nanoparticle sampling data using classical regression and ARIMA models

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Listed:
  • Seunghon Ham
  • Sunju Kim
  • Naroo Lee
  • Pilje Kim
  • Igchun Eom
  • Byoungcheun Lee
  • Perng-Jy Tsai
  • Kiyoung Lee
  • Chungsik Yoon

Abstract

Real-time monitoring is necessary for nanoparticle exposure assessment to characterize the exposure profile, but the data produced are autocorrelated. This study was conducted to compare three statistical methods used to analyze data, which constitute autocorrelated time series, and to investigate the effect of averaging time on the reduction of the autocorrelation using field data. First-order autoregressive (AR(1)) and autoregressive-integrated moving average (ARIMA) models are alternative methods that remove autocorrelation. The classical regression method was compared with AR(1) and ARIMA. Three data sets were used. Scanning mobility particle sizer data were used. We compared the results of regression, AR(1), and ARIMA with averaging times of 1, 5, and 10 min. AR(1) and ARIMA models had similar capacities to adjust autocorrelation of real-time data. Because of the non-stationary of real-time monitoring data, the ARIMA was more appropriate. When using the AR(1), transformation into stationary data was necessary. There was no difference with a longer averaging time. This study suggests that the ARIMA model could be used to process real-time monitoring data especially for non-stationary data, and averaging time setting is flexible depending on the data interval required to capture the effects of processes for occupational and environmental nano measurements.

Suggested Citation

  • Seunghon Ham & Sunju Kim & Naroo Lee & Pilje Kim & Igchun Eom & Byoungcheun Lee & Perng-Jy Tsai & Kiyoung Lee & Chungsik Yoon, 2017. "Comparison of data analysis procedures for real-time nanoparticle sampling data using classical regression and ARIMA models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 685-699, March.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:4:p:685-699
    DOI: 10.1080/02664763.2016.1182132
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    References listed on IDEAS

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    1. E. Andres Houseman & Louise Ryan & Jonathan Levy & John Spengler, 2002. "Autocorrelation in real-time continuous monitoring of microenvironments," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(6), pages 855-872.
    2. T. Ozaki, 1977. "On the Order Determination of Arima Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 26(3), pages 290-301, November.
    3. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    4. Mingfeng Lin & Henry C. Lucas & Galit Shmueli, 2013. "Research Commentary ---Too Big to Fail: Large Samples and the p -Value Problem," Information Systems Research, INFORMS, vol. 24(4), pages 906-917, December.
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