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.
| Original language | English |
|---|---|
| Pages (from-to) | 685-699 |
| Number of pages | 15 |
| Journal | Journal of Applied Statistics |
| Volume | 44 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2017 Mar 12 |
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All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Statistics, Probability and Uncertainty
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Comparison of data analysis procedures for real-time nanoparticle sampling data using classical regression and ARIMA models. / Ham, Seunghon; Kim, Sunju; Lee, Naroo; Kim, Pilje; Eom, Igchun; Lee, Byoungcheun; Tsai, Peng-Chi; Lee, Kiyoung; Yoon, Chungsik.
In: Journal of Applied Statistics, Vol. 44, No. 4, 12.03.2017, p. 685-699.Research output: Contribution to journal › Article
TY - JOUR
T1 - Comparison of data analysis procedures for real-time nanoparticle sampling data using classical regression and ARIMA models
AU - Ham, Seunghon
AU - Kim, Sunju
AU - Lee, Naroo
AU - Kim, Pilje
AU - Eom, Igchun
AU - Lee, Byoungcheun
AU - Tsai, Peng-Chi
AU - Lee, Kiyoung
AU - Yoon, Chungsik
PY - 2017/3/12
Y1 - 2017/3/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84966714424&partnerID=8YFLogxK
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U2 - 10.1080/02664763.2016.1182132
DO - 10.1080/02664763.2016.1182132
M3 - Article
AN - SCOPUS:84966714424
VL - 44
SP - 685
EP - 699
JO - Journal of Applied Statistics
JF - Journal of Applied Statistics
SN - 0266-4763
IS - 4
ER -