Survival analysis is concerned with finding models to predict the survival of patients or to assess the efficacy of a clinical treatment. A key part of the model-building process is the selection of the predictor variables. It is standard to use a stepwise procedure guided by a series of significance tests to select a single model, and then to make inference conditionally on the selected model. However, this ignores model uncertainty, which can be substantial. We review the standard Bayesian model averaging solution to this problem and extend it to survival analysis, introducing partial Bayes factors to do so for the Cox proportional hazards model. In two examples, taking account of model uncertainty enhances predictive performance, to an extent that could be clinically useful. 1 Introduction From 1974 to 1984 the Mayo Clinic conducted a double-blinded randomized clinical trial involving 312 patients to compare the drug DPCA with a placebo in the treatment of primary biliary cirrhosis...

We investigate the issue of model uncertainty in cross-country growth regressions using Bayesian Model Averaging (BMA). We find that the posterior probability is spread widely among many models, suggesting the superiority of BMA over choosing any single model. Out-of-sample predictive results support this claim. In contrast to Levine and Renelt (1992), our results broadly support the more ‘optimistic ’ conclusion of Salai-Martin (1997b), namely that some variables are important regressors for explaining cross-country growth patterns. However, care should be taken in the methodology employed. The approach proposed here is firmly grounded in statistical theory and immediately leads to posterior and predictive inference. Copyright © 2001 John Wiley & Sons, Ltd. 1.

This paper summarizes several classes of software cost estimation models and techniques: parametric models, expertise-based techniques, learning-oriented techniques, dynamics-based models, regression-based models, and composite-Bayesian techniques for integrating expertise-based and regression-based models. Experience to date indicates that neural-net and dynamics-based techniques are less mature than the other classes of techniques, but that all classes of techniques are challenged by the rapid pace of change in software technology. The primary conclusion is that no single technique is best for all situations, and that a careful comparison of the results of several approaches is most likely to produce realistic estimates. 1.

Data mining is on the interface of Computer Science and Statistics, utilizing advances in both disciplines to make progress in extracting information from large databases. It is an emerging field that has attracted much attention in a very short period of time. This article highlights some statistical themes and lessons that are directly relevant to data mining and attempts to identify opportunities where close cooperation between the statistical and computational communities might reasonably provide synergy for further progress in data analysis.

The rational choice framework assumes that individuals know what is in their self interest and make choices accordingly. Do they? When they go to the supermarket (in a developed country with a market economy) arguably they do act accordingly. In such settings, the individual knows, almost certainly, whether the choice would be beneficial, ex post. Indeed financial markets in the developed market economies (usually) possess the essential characteristics consistent with substantive rationality. However, it is simply not possible to make sense out of the diverse performance of economies and polities both historically and contemporaneously if individuals really knew their self interest and acted accordingly. Instead people act in part upon the basis of myths, dogmas, ideologies and "half-baked " theories. We argue here both that ideas matter, and that the way that ideas are communicated among people is crucial to building useful theories that will enable us to deal with strong uncertainty problems at the individual level.2 For most of the interesting issues in political and economic markets uncertainty, not risk, characterizes choice-making. Under conditions of uncertainty, individuals ' interpretation of their environment will reflect the

. Motion segmentation involves identifying regions of the image that correspond to independently moving objects. The number of independently moving objects, and type of motion model for each of the objects is unknown a priori. In order to perform motion segmentation, the problems of model selection, robust estimation and clustering must all be addressed simultaneously. Here we place the three problems into a common Bayesian framework; investigating the use of model averaging-representing a motion by a combination of models---as a principled way for motion segmentation of images. The final result is a fully automatic algorithm for clustering that works in the presence of noise and outliers. 1 Introduction Detection of independently moving objects is an essential but often neglected precursor to problems in computer vision e.g. e#cient video compression [3], video editing, surveillance, smart tracking of objects etc. The work in this paper stems from the desire to develop a g...

The analysis of spatial data has always played a central role in the quantitative scientific tradition in geography. Recently, there have appeared a considerable number of publications devoted to presenting research results and to assessing the state of the art.

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This paper proposes a Bayesian approach to a vector autoregression with stochastic volatility, where the multiplicative evolution of the precision matrix is driven by a multivariate beta variate. Exact updating formulas are given to the nonlinear filtering of the precision matrix. Estimation of the autoregressive parameters requires numerical methods: an importance-sampling based approach is explained here.