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Comparison of Methods for Smoothing Environmental Data with an Application to Particulate Matter PM10

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  • Martina Čampulová

    (Department of Statistics and Operation Analysis, Faculty of Business and Economics, Mendel University in Brno, Zemědělská 1, 613 00, Brno, Czech Republic)

Abstract

Data smoothing is often required within the environmental data analysis. A number of methods and algorithms that can be applied for data smoothing have been proposed. This paper gives an overview and compares the performance of different smoothing procedures that estimate the trend in the data, based on the surrounding noisy observations that can be applied on environmental data.The considered methods include kernel regression with both global and local bandwidth, moving average, exponential smoothing, robust repeated median regression, trend filtering and approach based on discrete Fourier and discrete wavelet transform. The methods are applied to real data obtained by measurement of PM10 concentrations and compared in a simulation study.

Suggested Citation

  • Martina Čampulová, 2018. "Comparison of Methods for Smoothing Environmental Data with an Application to Particulate Matter PM10," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 66(2), pages 453-463.
    • Handle: RePEc:mup:actaun:actaun_2018066020453
    DOI: 10.11118/actaun201866020453
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    References listed on IDEAS

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    1. Xiao Wang & Pang Du & Jinglai Shen, 2013. "Smoothing splines with varying smoothing parameter," Biometrika, Biometrika Trust, vol. 100(4), pages 955-970.
    2. Robert Tibshirani & Michael Saunders & Saharon Rosset & Ji Zhu & Keith Knight, 2005. "Sparsity and smoothness via the fused lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(1), pages 91-108, February.
    3. Fried, Roland & Gather, Ursula, 2004. "Methods and algorithms for robust filtering," Technical Reports 2004,44, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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