25,459 research outputs found
Bayesian shrinkage in mixture-of-experts models: identifying robust determinants of class membership
A method for implicit variable selection in mixture-of-experts frameworks is proposed.
We introduce a prior structure where information is taken from a set of independent
covariates. Robust class membership predictors are identified using a normal gamma
prior. The resulting model setup is used in a finite mixture of Bernoulli distributions
to find homogenous clusters of women in Mozambique based on their information
sources on HIV. Fully Bayesian inference is carried out via the implementation of a
Gibbs sampler
Clustering student skill set profiles in a unit hypercube using mixtures of multivariate betas
<br>This paper presents a finite mixture of multivariate betas as a new model-based clustering method tailored to applications where the feature space is constrained to the unit hypercube. The mixture component densities are taken to be conditionally independent, univariate unimodal beta densities (from the subclass of reparameterized beta densities given by Bagnato and Punzo 2013). The EM algorithm used to fit this mixture is discussed in detail, and results from both this beta mixture model and the more standard Gaussian model-based clustering are presented for simulated skill mastery data from a common cognitive diagnosis model and for real data from the Assistment System online mathematics tutor (Feng et al 2009). The multivariate beta mixture appears to outperform the standard Gaussian model-based clustering approach, as would be expected on the constrained space. Fewer components are selected (by BIC-ICL) in the beta mixture than in the Gaussian mixture, and the resulting clusters seem more reasonable and interpretable.</br>
<br>This article is in technical report form, the final publication is available at http://www.springerlink.com/openurl.asp?genre=article &id=doi:10.1007/s11634-013-0149-z</br>
Leveraging Contact Network Information in Clustered Randomized Studies of Contagion Processes
In a randomized study, leveraging covariates related to the outcome (e.g.
disease status) may produce less variable estimates of the effect of exposure.
For contagion processes operating on a contact network, transmission can only
occur through ties that connect affected and unaffected individuals; the
outcome of such a process is known to depend intimately on the structure of the
network. In this paper, we investigate the use of contact network features as
efficiency covariates in exposure effect estimation. Using augmented
generalized estimating equations (GEE), we estimate how gains in efficiency
depend on the network structure and spread of the contagious agent or behavior.
We apply this approach to simulated randomized trials using a stochastic
compartmental contagion model on a collection of model-based contact networks
and compare the bias, power, and variance of the estimated exposure effects
using an assortment of network covariate adjustment strategies. We also
demonstrate the use of network-augmented GEEs on a clustered randomized trial
evaluating the effects of wastewater monitoring on COVID-19 cases in
residential buildings at the the University of California San Diego.Comment: Substantial revisio
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