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Projected Statistical Methods for Distributional Data on the Real Line
  with the Wasserstein Metric
v1v2 (latest)

Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric

Journal of machine learning research (JMLR), 2021
22 January 2021
M. Pegoraro
Mario Beraha
ArXiv (abs)PDFHTML

Papers citing "Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric"

3 / 3 papers shown
DFNN: A Deep Fréchet Neural Network Framework for Learning Metric-Space-Valued Responses
DFNN: A Deep Fréchet Neural Network Framework for Learning Metric-Space-Valued Responses
Kyum Kim
Yaqing Chen
Paromita Dubey
157
2
0
20 Oct 2025
Wasserstein Principal Component Analysis for Circular Measures
Wasserstein Principal Component Analysis for Circular MeasuresStatistics and computing (Stat. Comput.), 2023
Mario Beraha
M. Pegoraro
238
3
0
05 Apr 2023
Efficient Convex PCA with applications to Wasserstein geodesic PCA and
  ranked data
Efficient Convex PCA with applications to Wasserstein geodesic PCA and ranked dataJournal of Computational And Graphical Statistics (JCGS), 2022
Steven Campbell
Ting-Kam Leonard Wong
166
2
0
05 Nov 2022
1
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