Jay M. Ver Hoef

Statistics and Probability · 1996–2017

A researcher in the Science corpus: 16 papers; trusts 32 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 36 citations into it; #3,734 of 12,932 by trust here, #9,233 by citations
• Ecology — 29 citations into it; #2,295 of 10,717 by trust here, #2,470 by citations
• Artificial Intelligence — 17 citations into it
• Nature and Landscape Conservation — 16 citations into it; #3,027 of 6,941 by trust here, #2,076 by citations
• Environmental Engineering — 14 citations into it; #632 of 5,479 by trust here, #1,040 by citations
• Economics and Econometrics — 12 citations into it; #13,847 of 23,481 by trust here, #12,444 by citations
• Ecological Modeling — 9 citations into it
• Management Science and Operations Research — 8 citations into it

Whom Jay M. Ver Hoef trusts overall

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

1. Julian Besag
2. Montserrat Fuentes
3. G. Oehlert
4. Joanne Potts
5. D. Higdon
6. Diane Lambert
7. Andrew Gelman
8. Eric R. Ziegel
9. D. Heilbron
10. Jorma Rissanen

Who trusts Jay M. Ver Hoef overall

1. Micha Mandel
2. D. Higdon
3. Jonas Knape
4. Aaron M. Christ
5. Noel Cressie
6. Montserrat Fuentes
7. Harlan Campbell
8. Zheng Fang
9. Mauricio A Álvarez
10. Francky Fouedjio

Papers from Jay M. Ver Hoef's perspective

Ordered by how strongly the researchers Jay M. Ver Hoef trusts endorse each paper. Same corpus, a different order for every researcher.

1. Spatial Interaction and the Statistical Analysis of Lattice Systems
2. Estimating the Dimension of a Model
3. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
4. Sampling-Based Approaches to Calculating Marginal Densities
5. Generalized Linear Models
6. Information Theory and an Extension of the Maximum Likelihood Principle
7. Gaussian Markov Random Fields: Theory and Applications
8. Bayesian data analysis.
9. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)
10. A process-convolution approach to modelling temperatures in the North Atlantic Ocean

Their papers

• Flexible Spatial Models for Kriging and Cokriging Using Moving Averages and the Fast Fourier Transform (FFT) (2004) — 105 citations
• Spatial statistical models that use flow and stream distance (2006) — 339 citations
• Who Invented the Delta Method (2012) — 246 citations
• On the relationship between conditional (CAR) and simultaneous (SAR) autoregressive models (2017) — 65 citations
• A Moving Average Approach for Spatial Statistical Models of Stream Networks (2010) — 266 citations
• Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling. (2009) — 569 citations
• Quasi-Poisson vs. negative binomial regression: how should we model overdispersed count data? (2007) — 1,143 citations
• Space—time zero‐inflated count models of Harbor seals (2007) — 110 citations
• The sequential megafaunal collapse hypothesis: Testing with existing data (2006) — 115 citations
• Distribution and density of moose in relation to landscape characteristics: effects of scale (2005) — 108 citations
• A Bayesian hierarchical model for monitoring harbor seal changes in Prince William Sound, Alaska (2003) — 91 citations
• Climate change and caribou: effects of summer weather on forage (2002) — 114 citations
• Spatial statistics: Analysis of field experiments (2001) — 47 citations
• Life-history consequences of maternal condition in Alaskan moose (2000) — 341 citations
• Constructing and fitting models for cokriging and multivariable spatial prediction (1998)
• Blackbox Kriging: Spatial Prediction Without Specifying Variogram Models (1996)