Bayesian covariance selection in generalized linear mixed models

SUMMARY. The generalized linear mixed model (GLMM), which extends the generalized linear model (GLM) to incorporate random effects characterizing heterogeneity among subjects, is widely used in analyzing correlated and longitudinal data. Although there is often interest in identify-ing the subset of predictors that have random effects, random effects selection can be challenging, particularly when outcome distributions are non-normal. This article proposes a fully Bayesian approach to the problem of simultaneous selection of fixed and random effects in GLMMs. Inte-grating out the random effects induces a covariance structure on the multivariate outcome data, and an important problem which we also consider is that of covariance selection. Our approach relies on variable selection-type mixture priors for the components in a special LDU decomposition of the random effects covariance. A stochastic search MCMC algorithm is developed, which relies on Gibbs sampling, with Taylor series expansions used to approximate intractable integrals. Simu-lated data examples are presented for different exponential family distributions, and the approach is applied to discrete survival data from a time-to-pregnancy study

Market Transparency and Traders ’ Behavior: An Analysis on Euronext with Full Order Book Data

More and more order-driven markets now allow traders to submit hidden orders. This widespread practice results in the presence of quantities which are available in the limit order book but not disclosed to market participants. The main contribution of this paper is to show how this unobservable information affects traders ’ behavior. Extending previous empirical analyzes on order aggressiveness, we evidence that traders account not only for information displayed on the market screens but also for information they can infer from limit order book movements. The other contribution of this paper is to report large reductions in implicit transaction costs due to hidden depth available in the order book. Results are provided for 82 Euronext blue chips over a three-month period

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This paper reexamines data from the New York City school choice program, the largest and best implemented private school scholarship experiment yet conducted. In the experiment, low-income public school students in grades K-4 were eligible to participate in a series of lotteries for a private school scholarship in May 1997. Data were collected from students and their parents at baseline, and in the Spring of each of the next three years. Students with missing baseline test scores, which encompasses all those who were initially in Kindergarten and 11 percent of those initially in grades 1-4, were excluded from previous analyses of achievement, even though these students were tested in the follow-up years. In principle, random assignment would be expected to lead treatment status to be uncorrelated with all baseline characteristics. Including students with missing baseline test scores increases the sample size by 44 percent. For African American students, the only group to show a significant, positive effect of vouchers on achievement in past studies, the difference in average follow-up test scores between the treatment group (those offered a voucher) and control group (those not offered a voucher