<p>An important problem in the analysis of gene expression data is the identification of groups of features that are coherently expressed. For example, one often wishes to know whether a group of genes, clustered because ...
We formulate a novel approach to infer conditional independence models or Markov structure of a multivariate distribution. Specifically, our objective is to place informative prior distributions over graphs (decomposable ...
<p>The study of the effect of the environment (e.g., climate and land use) on disease typically relies on aggregate disease data collected by the government surveillance network. The usual approach to analyze these data, ...
<p>In many spatio-temporal applications a vector of covariates is measured alongside a spatio-temporal response. In such cases, the purpose of the statistical model is to quantify the change, in expectation or otherwise, ...
<p>Mixture modeling of continuous data is an extremely effective and popular method for density estimation and clustering. However as the size of the data grows, both in terms of dimension and number of observations, many ...
<p>Most panel surveys are subject to missing data problems caused by panel attrition. The Additive Non-ignorable (AN) model proposed by Hirano et al. (2001) utilizes refreshment samples in panel surveys to impute missing ...
<p>This dissertation focuses on solving some common problems associated with ecological field studies. In the core of the statistical methodology, lies spatial modeling that provides greater flexibility and improved ...
<p>A maximum likelihood template fitting procedure is performed by using Upsilon --> mu+mu- events to extract the momentum scale, a scale factor applied to measured momentum, of the CDF detector at Fermilab. The constructed ...
<p>A tree-structured multiplicative gamma process (TMGP) is developed, for inferring the depth of a tree-based factor-analysis model. This new model is coupled with the nested Chinese restaurant process, to nonparametrically ...
<p>The thesis develops nonparametric Bayesian models to handle incomplete categorical variables in data sets with high dimension using the framework of multiple imputation. It presents methods for ignorable missing data ...
<p>We propose nonparametric Bayesian models for supervised dimension</p><p>reduction and regression problems. Supervised dimension reduction is</p><p>a setting where one needs to reduce the dimensionality of the</p><p>predictors ...
<p>Bayesian nonparametric methods are useful for modeling data without having to define the complexity of the entire model a priori, but rather allowing for this complexity determined by the data. We consider novel ...
<p>This dissertation is devoted to building Bayesian models for complex data, which are geared toward specific inferential aspects of applied problems. This broad topic is explored via three methodological case-studies, ...
<p>Capturing high dimensional complex ensembles of data is becoming commonplace in a variety of application areas. Some examples include</p><p>biological studies exploring relationships between genetic mutations and ...
<p>Directional data, i.e., data collected in the form of angles or natural directions arise in many scientific fields, such as oceanography, climatology, geology, meteorology and biology to name a few. The non-Euclidean ...
<p>Functional magnetic resonance imaging (fMRI) is a major neuroimaging methodology and have greatly facilitate basic cognitive neuroscience research. However, there are multiple statistical challenges in the analysis of ...
<p>In environmental health studies air pollution measurements from the closest monitor are commonly used as a proxy for personal exposure. This technique assumes that air pollution concentrations are spatially homogeneous ...
<p>Novelistic genres--such as gothic novels, epistolary novels, and Bildungsromane--were an abiding feature of literary production in the nineteenth century. Their appearance, disappearance, and transmission across national ...
<p>Adversarial risk analysis (ARA) attempts to apply statistical methodology</p><p>to game-theoretic problems and provides an alternative to the solution concepts in traditional game theory. Specifically, it uses a Bayesian ...