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Two procedures for robust monitoring of probability distributions of economic data stream induced by depth functions

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  • Daniel Kosiorowski

Abstract

Data streams (streaming data) consist of transiently observed, evolving in time, multidimensional data sequences that challenge our computational and/or inferential capabilities. We propose user friendly approaches for robust monitoring of selected properties of unconditional and conditional distributions of the stream based on depth functions. Our proposals are robust to a small fraction of outliers and/or inliers, but at the same time are sensitive to a regime change in the stream. Their implementations are available in our free R package DepthProc.

Suggested Citation

  • Daniel Kosiorowski, 2015. "Two procedures for robust monitoring of probability distributions of economic data stream induced by depth functions," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 25(1), pages 55-79.
    • Handle: RePEc:wut:journl:v:1:y:2015:p:55-79:id:1116
    DOI: 10.5277/ord150104
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    References listed on IDEAS

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    5. Davy Paindaveine & Germain Van bever, 2013. "From Depth to Local Depth: A Focus on Centrality," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 1105-1119, September.
    6. Racine, Jeffrey S., 2008. "Nonparametric Econometrics: A Primer," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(1), pages 1-88, March.
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    1. Daniel Kosiorowski, 2014. "Functional Regression in Short-Term Prediction of Economic Time Series," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 15(4), pages 611-626, September.

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