Frequentist and Bayesian Change-Point Models: A Missing Link

Ardia, David; Dufays, Arnaud; Criado, Carlos Ordás

doi:10.2139/ssrn.3499824

Cited by 2 publications

(2 citation statements)

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“…Other criteria, such as, e.g., the Minimum Description Length (MDL) [51], were used by some authors in the specific climate context [19,31]. According to Ardia et al [52], the MDL criterion can be seen as a Bayesian criterion with an appropriate prior distribution for change point models. The obtained MDL-based penalties have thus essentially the same properties as the mBIC and were not considered in our framework.…”

Section: Discussionmentioning

confidence: 99%

GNSSseg, a Statistical Method for the Segmentation of Daily GNSS IWV Time Series

2022

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Homogenization is an important and crucial step to improve the usage of observational data for climate analysis. This work is motivated by the analysis of long series of GNSS Integrated Water Vapour (IWV) data, which have not yet been used in this context. This paper proposes a novel segmentation method called segfunc that integrates a periodic bias and a heterogeneous, monthly varying, variance. The method consists in estimating first the variance using a robust estimator and then estimating the segmentation and periodic bias iteratively. This strategy allows for the use of the dynamic programming algorithm, which is the most efficient exact algorithm to estimate the change point positions. The performance of the method is assessed through numerical simulation experiments. It is implemented in the R package GNSSseg, which is available on the CRAN. This paper presents the application of the method to a real data set from a global network of 120 GNSS stations. A hit rate of 32% is achieved with respect to available metadata. The final segmentation is made in a semi-automatic way, where the change points detected by three different penalty criteria are manually selected. In this case, the hit rate reaches 60% with respect to the metadata.

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Section: Discussionmentioning

confidence: 99%

GNSSseg, a Statistical Method for the Segmentation of Daily GNSS IWV Time Series

2022

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“…In the specific climate context, some authors as Li and Lund (2012) and Lu et al (2010) use a MDL based-criterion (Rissanen (1978)). Ardia et al (2019) show that the MDL criterion can be seen as a Bayesian criterion with appropriate prior distributions for change-point models. As a consequence, the obtained based-MDL penalties (see Li and Lund (2012); Lu et al (2010)) looks like the mBIC (their both penalties integrate a term depending on the segment lengths of the segmentation).…”

Section: Model and Inferencementioning

confidence: 99%

A new segmentation method for the homogenisation of GNSS-derived IWV time-series

Quarello

Bock

Lebarbier

2020

Preprint

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Homogenization is an important and crucial step to improve the usage of observational data for climate analysis. This work is motivated by the analysis of long series of GNSS Integrated Water Vapour (IWV) data which have not yet been used in this context. This paper proposes a novel segmentation method that integrates a periodic bias and a heterogeneous, monthly varying, variance. The method consists in estimating first the variance using a robust estimator and then estimating the segmentation and periodic bias iteratively. This strategy allows for the use of the dynamic programming algorithm that remains the most efficient exact algorithm to estimate the change-point positions. The statistical performance of the method is assessed through numerical experiments. An application to a real data set of 120 global GNSS stations is presented. The method is implemented in the R package GNSSseg that will be available on the CRAN.

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Frequentist and Bayesian Change-Point Models: A Missing Link

Cited by 2 publications

References 61 publications

GNSSseg, a Statistical Method for the Segmentation of Daily GNSS IWV Time Series

GNSSseg, a Statistical Method for the Segmentation of Daily GNSS IWV Time Series

A new segmentation method for the homogenisation of GNSS-derived IWV time-series

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