103 research outputs found
Inferring Hospital Quality from Patient Discharge Records Using a Bayesian Selection Model
Bayesian inference for finite mixtures of generalized linear models with random effects
We present an hierarchical Bayes approach to modeling parameter heterogeneity in generalized linear models. The model assumes that there are relevant subpopulations and that within each subpopulation the individual-level regression coefficients have a multivariate normal distribution. However, class membership is not known a priori, so the heterogeneity in the regression coefficients becomes a finite mixture of normal distributions. This approach combines the flexibility of semiparametric, latent class models that assume common parameters for each sub-population and the parsimony of random effects models that assume normal distributions for the regression parameters. The number of subpopulations is selected to maximize the posterior probability of the model being true. Simulations are presented which document the performance of the methodology for synthetic data with known heterogeneity and number of sub-populations. An application is presented concerning preferences for various aspects of personal computers.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45757/1/11336_2005_Article_BF02294188.pd
Exact and efficient Bayesian inference for multiple changepoint problems.
We demonstrate how to perform direct simulation from the posterior distribution of a class of multiple changepoint models where the number of changepoints is unknown. The class of models assumes independence between the posterior distribution of the parameters associated with segments of data between successive changepoints. This approach is based on the use of recursions, and is related to work on product partition models. The computational complexity of the approach is quadratic in the number of observations, but an approximate version, which introduces negligible error, and whose computational cost is roughly linear in the number of observations, is also possible. Our approach can be useful, for example within an MCMC algorithm, even when the independence assumptions do not hold. We demonstrate our approach on coal-mining disaster data and on well-log data. Our method can cope with a range of models, and exact simulation from the posterior distribution is possible in a matter of minutes
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Tobit estiamtion with unknown point of censoring with an application to milk market participation in the Ethiopian highlands
Data augmentation is a powerful technique for estimating models with latent or missing data, but applications
in agricultural economics have thus far been few. This paper showcases the technique in an
application to data on milk market participation in the Ethiopian highlands. There, a key impediment
to economic development is an apparently low rate of market participation. Consequently, economic
interest centers on the ālocationsā of nonparticipants in relation to the market and their āreservation
valuesā across covariates. These quantities are of policy interest because they provide measures of the
additional inputs necessary in order for nonparticipants to enter the market. One quantity of primary
interest is the minimum amount of surplus milk (the āminimum efficient scale of operationsā) that the
household must acquire before market participation becomes feasible. We estimate this quantity
through routine application of data augmentation and Gibbs sampling applied to a random-censored
Tobit regression. Incorporating random censoring affects markedly the marketable-surplus requirements
of the household, but only slightly the covariates requirements estimates and, generally, leads to
more plausible policy estimates than the estimates obtained from the zero-censored formulatio
Estimating the Cross-Sectional Market Response to an Endogenous Event: Naked vs. Underwritten Calls of Convertible Bonds
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