William Kleiber

Global and Planetary Change · 2010–2015

A researcher in the Science corpus: 5 papers; trusts 36 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Global and Planetary Change — 54 citations into it; #1,981 of 10,225 by trust here, #4,502 by citations
• Environmental Engineering — 51 citations into it; #1,608 of 5,479 by trust here, #2,998 by citations
• Atmospheric Science — 28 citations into it; #4,530 of 4,804 by trust here, #3,070 by citations
• Economics and Econometrics — 27 citations into it
• Statistics and Probability — 15 citations into it
• Artificial Intelligence — 14 citations into it
• Finance — 9 citations into it
• Management Science and Operations Research — 9 citations into it

Whom William Kleiber trusts overall

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

1. Denis Allard
2. Thomas M. Hamill
3. Michael L. Stein
4. D. Wilks
5. Tilmann Gneiting
6. Veronica J. Berrocal
7. Adrian E. Raftery
8. Alan E. Gelfand
9. Dick Dee
10. Michael E. Hohn

Who trusts William Kleiber overall

1. Mikyoung Jun
2. Andrew Verdin
3. Tilmann Gneiting
4. Kira Feldmann
5. Nina Schuhen
6. Thordis Linda Thorarinsdottir
7. Emilio Porcu
8. Balaji Rajagopalan
9. Chaopeng Shen
10. Tatiyana V. Apanasovich

Papers from William Kleiber's perspective

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

1. Interpolation of spatial data
2. Using Bayesian Model Averaging to Calibrate Forecast Ensembles
3. Spatial Interaction and the Statistical Analysis of Lattice Systems
4. Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging
5. Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems
6. Strictly Proper Scoring Rules, Prediction, and Estimation
7. Estimating the Dimension of a Model
8. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
9. The Use of Model Output Statistics (MOS) in Objective Weather Forecasting
10. Hierarchical Modeling and Analysis for Spatial Data

Their papers

• Spatial Bayesian hierarchical modeling of precipitation extremes over a large domain (2015) — 47 citations
• Coupled stochastic weather generation using spatial and generalized linear models (2015) — 61 citations
• Daily spatiotemporal precipitation simulation using latent and transformed Gaussian processes (2012) — 167 citations
• Locally Calibrated Probabilistic Temperature Forecasting Using Geostatistical Model Averaging and Local Bayesian Model Averaging (2011) — 54 citations
• Matérn Cross-Covariance Functions for Multivariate Random Fields (2010) — 460 citations