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The following zero-sum game between nature and a statistician blends Bayesian methods with frequentist methods such as p-values and confidence intervals. Nature chooses a posterior distribution consis...
A constrained L1 minimization method is proposed for estimating a sparse inverse covariance matrix based on a sample of $n$ iid $p$-variate random variables. The resulting estimator is shown to enjoy ...
Besides serving as prediction models, classification trees are use-ful for finding important predictor variables and identifying inter-esting subgroups in the data. These functions can be compromised ...
Residuals in regression models are often spatially correlated.Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants.
Empirical likelihood is a popular nonparametric or semi-parametric statistical method with many nice statistical properties. Yet when the sample size is small, or the dimension of the accompanying est...
The simplex parameterization has at least two attractive features. First, it typically leads to simple MCMC algorithms with good mixing proper- ties regardless of the parameterization used to specif...
We examine the spectral profile bound of Goel, Montenegro and Tetali - for the L mixing time of continuous-time random walk in reversible settings. We find that it is precise up to a loglog factor, an...

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