Bayesian Model Selection in Social Research
- A. Raftery
It is argued that P-values and the tests based upon them give unsatisfactory results, especially in large samples. It is shown that, in regression, when there are many candidate independent…
Strictly Proper Scoring Rules, Prediction, and Estimation
The theory of proper scoring rules on general probability spaces is reviewed and developed, and the intuitively appealing interval score is proposed as a utility function in interval estimation that addresses width as well as coverage.
Model-Based Clustering, Discriminant Analysis, and Density Estimation
This work reviews a general methodology for model-based clustering that provides a principled statistical approach to important practical questions that arise in cluster analysis, such as how many clusters are there, which clustering method should be used, and how should outliers be handled.
Model-based Gaussian and non-Gaussian clustering
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 criterion of Friedman and Rubin (1967), but it is restricted to Gaussian distributions and it does not allow for noise.
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 of…
Bayesian Model Averaging: A Tutorial
Bayesian model averaging (BMA) provides a coherent mechanism for ac- counting for this model uncertainty and provides improved out-of- sample predictive performance.
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 postprocessing…
How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
The problems of determining the number of clusters and the clustering method are solved simultaneously by choosing the best model, and the EM result provides a measure of uncertainty about the associated classification of each data point.
Latent Space Approaches to Social Network Analysis
This work develops a class of models where the probability of a relation between actors depends on the positions of individuals in an unobserved “social space,” and proposes Markov chain Monte Carlo procedures for making inference on latent positions and the effects of observed covariates.
Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation
This work proposes the use of ensemble model output statistics (EMOS), an easy-to-implement postprocessing technique that addresses both forecast bias and underdispersion and takes into account the spread-skill relationship.