Survival analysis is a supervised learning technique that in the context of microarray data is most frequently used to identify genes whose expression levels are correlated with patient survival prognosis. Survival analysis is generally applied to diseased samples for the purpose of analyzing time to event, where the event can be any milestone of interest (e.g., metastases, relapse, or death). ...

Classification is a supervised learning technique that in the context of microarray analysis is most frequently used to identify genes whose expression is correlated with specific phenotypes of the samples. Typically, the interest is in identifying genes that are predictive of disease. In such cases, both the accuracy of the prediction and the number of genes necessary to obtain a given accurac...

Bayesian model averaging (BMA) has become widely accepted as a way of accounting for model uncertainty, notably in regression models for identifying the determinants of economic growth. To implement BMA the user must specify a prior distribution in two parts: a prior for the regression parameters and a prior over the model space. Here we address the issue of which default prior to use for BMA i...

In this paper I estimate empirical growth models simultaneously considering endogenous regressors and model uncertainty. In order to apply Bayesian methods such as Bayesian Model Averaging (BMA) to dynamic panel data models with predetermined or endogenous variables and fixed effects, I propose a likelihood function for such models. The resulting maximum likelihood estimator can be interpreted ...

A Bayesian approach is used to estimate a nonparametric regression model. The main features of the procedure are, first, the functional form of the curve is approximated by a mixture of local polynomials by Bayesian Model Averaging (BMA); second, the model weights are approximated by the BIC criterion, and third, a robust estimation procedure is incorporated to improve the smoothness of the est...

The MC3 (Madigan and York, 1995) and Gibbs (George and McCulloch, 1997) samplers are the most widely implemented algorithms for Bayesian Model Averaging (BMA) in linear regression models. These samplers draw a variable at random in each iteration using uniform selection probabilities and then propose to update that variable. This may be computationally inefficient if the number of variables is ...

We investigate the role of initial conditions at colonial independence on economic growth in Africa in the post-independence period using Bayesian Model Averaging (BMA). A key innovation in our estimation methodology is that we incorporate parameter heterogeneity in model averaging as well as try to mitigate the endogeneity problem present in growth regressions. In order to ensure that differen...

X-chromosome is often excluded from the so called ‘wholegenome’ association studies due to its intrinsic difference between males and females. One particular analytical challenge is the unknown status of X-inactivation, where one of the two X-chromosome variants in females may be randomly selected to be silenced. In the absence of biological evidence in favour of one specific model, we consider...

Predicting the evolution of mortality rates plays a central role for life insurance and pension funds. Various stochastic frameworks have been developed to model patterns by taking into account main stylized facts driving these patterns. However, relying on prediction one specific can be too restrictive lead some well-documented drawbacks, including misspecification, parameter uncertainty, over...