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

DOI10.1080/02664763.2016.1182132OpenAlexW2371049389MaRDI QIDQ5138573

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

Publication date: 4 December 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://figshare.com/articles/journal_contribution/Comparison_of_data_analysis_procedures_for_real_time_nanoparticle_sampling_data_using_classical_regression_and_ARIMA_models/3380038

zbMATH Keywords

time seriesARIMAautocorrelationnanoparticlereal-time monitoringoccupational and environmental nano measurementsparticulate matter monitoringultrafine particle

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• Unnamed Item
• car
• lmtest
• nlme
• forecast
• Forecast
• Stoffenmanager Nano
• carData

Cites Work

• Unnamed Item
• Testing the null hypothesis of stationarity against the alternative of a unit root. How sure are we that economic time series have a unit root?
• Introductory Time Series with R

This page was built for publication: Comparison of data analysis procedures for real-time nanoparticle sampling data using classical regression and ARIMA models