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Nonparametric data segmentation in multivariate time series via joint characteristic functions
E T Mcgonigle and H Cho
Biometrika, asaf024, https://doi.org/10.1093/biomet/asaf024
Modern time series data often exhibit complex dependence and structural changes which are not easily characterized by shifts in the mean or model parameters. We propose a nonparametric data segmentation methodology for multivariate time series. By considering joint characteristic functions between ...
An Improved Estimator of the Pair Correlation Function of a Spatial Point Process
A Baddeley and others
Biometrika, asaf021, https://doi.org/10.1093/biomet/asaf021
The pair correlation function (two-point correlation) of a spatial point process is a fundamental tool in spatial statistics and astrostatistics, measuring the strength of spatial dependence between points. Interest focuses on the behaviour of this function at short distances, but this is the ...
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第19回日本統計学会春季集会の報告を掲載しました。
2025.03.16
第19回日本統計学会春季集会の報告を掲載しました
2025.03.16
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Efficient inference for spatial extreme value processes associated to log-Gaussian random functions
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22nd ConferenceJuly 13- 16 2006Cambridge, MA, USA