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- Publications
- Influence

Strictly Proper Scoring Rules, Prediction, and Estimation

- T. Gneiting, A. Raftery
- Mathematics
- 1 March 2007

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 proper… Expand

Model-Based Clustering, Discriminant Analysis, and Density Estimation

- C. Fraley, A. Raftery
- Mathematics
- 1 June 2002

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, and… Expand

Model-based Gaussian and non-Gaussian clustering

- Jeffrey D. Banfield, A. Raftery
- Mathematics
- 1 September 1993

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 the… Expand

Using Bayesian Model Averaging to Calibrate Forecast Ensembles

- A. Raftery, T. Gneiting, F. Balabdaoui, M. Polakowski
- Mathematics
- 1 May 2005

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 postprocessing… Expand

How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis

- C. Fraley, A. Raftery
- Computer Science
- Comput. J.
- 1998

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 a… Expand

Latent Space Approaches to Social Network Analysis

- Peter D. Hoff, A. Raftery, Mark S. Handcock
- Mathematics
- 1 December 2002

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 nodes… Expand

Probabilistic forecasts, calibration and sharpness

- T. Gneiting, F. Balabdaoui, A. Raftery
- Mathematics
- 1 April 2007

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 of… Expand

Bayesian Model Averaging: A Tutorial

- J. Hoeting, D. Madigan, A. Raftery, C. Volinsky
- Computer Science
- 2016

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. This… Expand

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Bayesian Model Averaging for Linear Regression Models

- A. Raftery, D. Madigan, J. Hoeting
- Mathematics
- 1 March 1997

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 the… Expand

Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation

- T. Gneiting, A. Raftery, Anton H. Westveld, T. Goldman
- Mathematics
- 1 May 2005

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 proposes… Expand