The Ljung-Box test is typically used to test serial independence even if, by construction, it is generally powerful only in presence of pairwise linear dependence between lagged variables. To overcome this problem, Bagnato et al recently proposed a simple statistic defining a serial independence test which, differently from the Ljung-Box test, is powerful also under a linear/nonlinear dependent process characterized by pairwise independence. The authors also introduced a normalized bar diagram, based on p-values from the proposed test, to investigate serial dependence. This paper proposes a balanced normalization of such a diagram taking advantage of the concept of reproducibility probability. This permits to study the strength and the stability of the evidence about the presence of the dependence under investigation. An illustrative example based on an artificial time series, as well as an application to a transport time series, are considered to appreciate the usefulness of the proposal.

Bagnato, L., De Capitani, L., Punzo, A., A diagram to detect serial dependencies: an application to transport time series, <<QUALITY & QUANTITY>>, 2017; 51 (2): 581-594. [doi:10.1007/s11135-016-0426-y] [http://hdl.handle.net/10807/86145]

A diagram to detect serial dependencies: an application to transport time series

Bagnato, Luca
Primo
;
2017

Abstract

The Ljung-Box test is typically used to test serial independence even if, by construction, it is generally powerful only in presence of pairwise linear dependence between lagged variables. To overcome this problem, Bagnato et al recently proposed a simple statistic defining a serial independence test which, differently from the Ljung-Box test, is powerful also under a linear/nonlinear dependent process characterized by pairwise independence. The authors also introduced a normalized bar diagram, based on p-values from the proposed test, to investigate serial dependence. This paper proposes a balanced normalization of such a diagram taking advantage of the concept of reproducibility probability. This permits to study the strength and the stability of the evidence about the presence of the dependence under investigation. An illustrative example based on an artificial time series, as well as an application to a transport time series, are considered to appreciate the usefulness of the proposal.
Inglese
Bagnato, L., De Capitani, L., Punzo, A., A diagram to detect serial dependencies: an application to transport time series, <>, 2017; 51 (2): 581-594. [doi:10.1007/s11135-016-0426-y] [http://hdl.handle.net/10807/86145]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10807/86145
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