Qingyun Duan

Statistics and Probability · 1992–2020

A researcher in the Science corpus: 18 papers; trusts 44 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 94 citations into it
• Global and Planetary Change — 83 citations into it; #2,126 of 10,225 by trust here, #2,967 by citations
• Management Science and Operations Research — 46 citations into it
• Computational Theory and Mathematics — 42 citations into it
• Atmospheric Science — 40 citations into it
• Water Science and Technology — 39 citations into it; #165 of 1,711 by trust here, #384 by citations
• Environmental Engineering — 36 citations into it; #2,296 of 5,479 by trust here, #1,430 by citations
• Artificial Intelligence — 34 citations into it

Whom Qingyun Duan trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Soroosh Sorooshian
2. Adrian E. Raftery
3. Vinod Kumar Gupta
4. C. Tebaldi
5. Shlomo P. Neuman
6. Keith Beven
7. Geoffrey J. McLachlan
8. Tim Hesterberg
9. John A. Nelder
10. T. Reichler

Who trusts Qingyun Duan overall

1. Jasper A. Vrugt
2. Hoshin V. Gupta
3. Dmitri Kavetski
4. Bryan Tolson
5. Thorsten Wagener
6. Soroosh Sorooshian
7. Henrik Madsen
8. Keith Beven
9. Jing Yang
10. Christine Annette Shoemaker

Papers from Qingyun Duan's perspective

Ordered by how strongly the researchers Qingyun Duan trusts endorse each paper. Same corpus, a different order for every researcher.

1. Effective and efficient global optimization for conceptual rainfall‐runoff models
2. Using Bayesian Model Averaging to Calibrate Forecast Ensembles
3. The future of distributed models: model calibration and uncertainty prediction.
4. Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors
5. The EM algorithm and extensions
6. Towards reduced uncertainty in conceptual rainfall‐runoff modelling: dynamic identifiability analysis
7. The use of the multi-model ensemble in probabilistic climate projections (Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences)
8. Monte Carlo Sampling Methods Using Markov Chains and Their Applications
9. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
10. Quantification of modelling uncertainties in a large ensemble of climate change simulations (Nature)

Their papers

• An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014) — 252 citations
• The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020) — 552 citations
• Global surface air temperatures in CMIP6: historical performance and future changes (2020) — 233 citations
• A nonstationary bias‐correction technique to remove bias in GCM simulations (2016) — 149 citations
• An Intercomparison of Sampling Methods for Uncertainty Quantification of Environmental Dynamic Models (2015) — 52 citations
• A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model (2014) — 342 citations
• A global soil data set for earth system modeling (2014) — 604 citations
• Assessment of CMIP5 climate models and projected temperature changes over Northern Eurasia (2014) — 215 citations
• Assessing parameter importance of the Common Land Model based on qualitative and quantitative sensitivity analysis (2013) — 89 citations
• Bayesian estimation of local signal and noise in multimodel simulations of climate change (2010) — 141 citations
• Multi-model ensemble hydrologic prediction using Bayesian model averaging (2007) — 687 citations
• Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops (2006) — 441 citations
• An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction (2006) — 599 citations
• Surface radiation budgets in support of the GEWEX Continental‐Scale International Project (GCIP) and the GEWEX Americas Prediction Project (GAPP), including the North American Land Data Assimilation System (NLDAS) project (2003) — 238 citations
• Optimal use of the SCE-UA global optimization method for calibrating watershed models (1994)
• Shuffled complex evolution approach for effective and efficient global minimization (1993)
• Calibration of rainfall‐runoff models: Application of global optimization to the Sacramento Soil Moisture Accounting Model (1993)
• Effective and efficient global optimization for conceptual rainfall‐runoff models (1992)