• Publications
  • Influence
Strictly Proper Scoring Rules, Prediction, and Estimation
Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the predictive distribution and on the event or value that materializes. A scoring rule is properExpand
  • 2,764
  • 403
Model-Based Clustering, Discriminant Analysis, and Density Estimation
Cluster analysis is the automated search for groups of related observations in a dataset. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures, andExpand
  • 3,383
  • 282
Model-based Gaussian and non-Gaussian clustering
Abstract : The classification maximum likelihood approach is sufficiently general to encompass many current clustering algorithms, including those based on the sum of squares criterion and on theExpand
  • 2,014
  • 191
Using Bayesian Model Averaging to Calibrate Forecast Ensembles
Ensembles used for probabilistic weather forecasting often exhibit a spread-error correlation, but they tend to be underdispersive. This paper proposes a statistical method for postprocessingExpand
  • 1,216
  • 158
How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
We consider the problem of determining the structure of clustered data, without prior knowledge of the number of clusters or any other information about their composition. Data are represented by aExpand
  • 2,275
  • 149
Latent Space Approaches to Social Network Analysis
Network models are widely used to represent relational information among interacting units. In studies of social networks, recent emphasis has been placed on random graph models where the nodesExpand
  • 1,422
  • 149
Probabilistic forecasts, calibration and sharpness
Summary. Probabilistic forecasts of continuous variables take the form of predictive densities or predictive cumulative distribution functions. We propose a diagnostic approach to the evaluation ofExpand
  • 951
  • 143
Bayesian Model Averaging: A Tutorial
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from some class of models and then proceed as if the selected model had generated the data. ThisExpand
  • 1,198
  • 135
Bayesian Model Averaging for Linear Regression Models
Abstract We consider the problem of accounting for model uncertainty in linear regression models. Conditioning on a single selected model ignores model uncertainty, and thus leads to theExpand
  • 1,530
  • 112
Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation
Abstract Ensemble prediction systems typically show positive spread-error correlation, but they are subject to forecast bias and dispersion errors, and are therefore uncalibrated. This work proposesExpand
  • 601
  • 104