9,111 research outputs found
Bayesian Conditional Tensor Factorizations for High-Dimensional Classification
In many application areas, data are collected on a categorical response and
high-dimensional categorical predictors, with the goals being to build a
parsimonious model for classification while doing inferences on the important
predictors. In settings such as genomics, there can be complex interactions
among the predictors. By using a carefully-structured Tucker factorization, we
define a model that can characterize any conditional probability, while
facilitating variable selection and modeling of higher-order interactions.
Following a Bayesian approach, we propose a Markov chain Monte Carlo algorithm
for posterior computation accommodating uncertainty in the predictors to be
included. Under near sparsity assumptions, the posterior distribution for the
conditional probability is shown to achieve close to the parametric rate of
contraction even in ultra high-dimensional settings. The methods are
illustrated using simulation examples and biomedical applications
Bayesian Tobit quantile regression using-prior distribution with ridge parameter
A Bayesian approach is proposed for coefficient estimation in the Tobit quantile regression model. The
proposed approach is based on placing a g-prior distribution depends on the quantile level on the regression
coefficients. The prior is generalized by introducing a ridge parameter to address important challenges
that may arise with censored data, such as multicollinearity and overfitting problems. Then, a stochastic
search variable selection approach is proposed for Tobit quantile regression model based on g-prior. An
expression for the hyperparameter g is proposed to calibrate the modified g-prior with a ridge parameter to
the corresponding g-prior. Some possible extensions of the proposed approach are discussed, including the
continuous and binary responses in quantile regression. The methods are illustrated using several simulation
studies and a microarray study. The simulation studies and the microarray study indicate that the proposed
approach performs well
Optimal treatment allocations in space and time for on-line control of an emerging infectious disease
A key component in controlling the spread of an epidemic is deciding where, whenand to whom to apply an intervention.We develop a framework for using data to informthese decisionsin realtime.We formalize a treatment allocation strategy as a sequence of functions, oneper treatment period, that map up-to-date information on the spread of an infectious diseaseto a subset of locations where treatment should be allocated. An optimal allocation strategyoptimizes some cumulative outcome, e.g. the number of uninfected locations, the geographicfootprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategyfor an emerging infectious disease is challenging because spatial proximity induces interferencebetween locations, the number of possible allocations is exponential in the number oflocations, and because disease dynamics and intervention effectiveness are unknown at outbreak.We derive a Bayesian on-line estimator of the optimal allocation strategy that combinessimulation–optimization with Thompson sampling.The estimator proposed performs favourablyin simulation experiments. This work is motivated by and illustrated using data on the spread ofwhite nose syndrome, which is a highly fatal infectious disease devastating bat populations inNorth America
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