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11
Statistical Postprocessing for Weather Forecasts: Review, Challenges, and Avenues in a Big Data World
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Statistical Postprocessing for Weather Forecasts: Review, Challenges, and Avenues in a Big Data World

Vannitsem, Stéphane ; Bremnes, John Bjørnar ; Demaeyer, Jonathan ; Evans, Gavin R. ; Flowerdew, Jonathan ; Hemri, Stephan ; Lerch, Sebastian ; Roberts, Nigel ; Theis, Susanne ; Atencia, Aitor ; Bouallègue, Zied Ben ; Bhend, Jonas ; Dabernig, Markus ; De Cruz, Lesley ; Hieta, Leila ; Mestre, Olivier ; Moret, Lionel ; Plenković, Iris Odak ; Schmeits, Maurice ; Taillardat, Maxime ; Van den Bergh, Joris ; Van Schaeybroeck, Bert ; Whan, Kirien ; Ylhaisi, Jussi

Bulletin of the American Meteorological Society, 2021-03, Vol.102 (3), p.E681-E699 [Periódico revisado por pares]

Boston: American Meteorological Society

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12
Incentives to Exercise
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Incentives to Exercise

Charness, Gary ; Gneezy, Uri

Econometrica, 2009-05, Vol.77 (3), p.909-931 [Periódico revisado por pares]

Oxford, UK: Blackwell Publishing Ltd

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13
A New Coefficient of Correlation
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A New Coefficient of Correlation

Chatterjee, Sourav

Journal of the American Statistical Association, 2021-10, Vol.116 (536), p.2009-2022 [Periódico revisado por pares]

Alexandria: Taylor & Francis

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14
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
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Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

Wager, Stefan ; Athey, Susan

Journal of the American Statistical Association, 2018-07, Vol.113 (523), p.1228-1242 [Periódico revisado por pares]

Alexandria: Taylor & Francis

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15
Optimal transport-based machine learning to match specific patterns: application to the detection of molecular regulation patterns in omics data
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Optimal transport-based machine learning to match specific patterns: application to the detection of molecular regulation patterns in omics data

Nguyen, Thi Thanh Yen ; Harchaoui, Warith ; Mégret, Lucile ; Mendoza, Cloé ; Bouaziz, Olivier ; Neri, Christian ; Chambaz, Antoine

Journal of the Royal Statistical Society Series C: Applied Statistics, 2024-06, Vol.73 (3), p.639-657 [Periódico revisado por pares]

Wiley

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16
Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors
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Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors

Simpson, Daniel ; Rue, Håvard ; Riebler, Andrea ; Martins, Thiago G. ; Sørbye, Sigrunn H.

Statistical science, 2017-02, Vol.32 (1), p.1-28 [Periódico revisado por pares]

Hayward: Institute of Mathematical Statistics

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17
Balancing Covariates via Propensity Score Weighting
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Balancing Covariates via Propensity Score Weighting

Li, Fan ; Morgan, Kari Lock ; Zaslavsky, Alan M.

Journal of the American Statistical Association, 2018-01, Vol.113 (521), p.390-400 [Periódico revisado por pares]

Alexandria: Taylor & Francis

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18
A Minimax Optimal Ridge-Type Set Test for Global Hypothesis With Applications in Whole Genome Sequencing Association Studies
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A Minimax Optimal Ridge-Type Set Test for Global Hypothesis With Applications in Whole Genome Sequencing Association Studies

Liu, Yaowu ; Li, Zilin ; Lin, Xihong

Journal of the American Statistical Association, 2022-04, Vol.117 (538), p.897-908 [Periódico revisado por pares]

Alexandria: Taylor & Francis

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19
A simple new approach to variable selection in regression, with application to genetic fine mapping
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A simple new approach to variable selection in regression, with application to genetic fine mapping

Wang, Gao ; Sarkar, Abhishek ; Carbonetto, Peter ; Stephens, Matthew

Journal of the Royal Statistical Society. Series B, Statistical methodology, 2020-12, Vol.82 (5), p.1273-1300 [Periódico revisado por pares]

England: Oxford University Press

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20
Visualizing the effects of predictor variables in black box supervised learning models
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Visualizing the effects of predictor variables in black box supervised learning models

Apley, Daniel W. ; Zhu, Jingyu

Journal of the Royal Statistical Society. Series B, Statistical methodology, 2020-09, Vol.82 (4), p.1059-1086 [Periódico revisado por pares]

Oxford: Oxford University Press

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