<p>I consider the problem of clustering multiple related groups of data. My approach entails mixture models in the context of hierarchical Dirichlet processes, focusing on their ability to perform inference on the unknown ...
<p>Our interest is the risk assessment of rare natural hazards, such as</p><p>large volcanic pyroclastic flows. Since catastrophic consequences of</p><p>volcanic flows are rare events, our analysis benefits from the use ...
<p>If a distant star happens to host an orbiting exoplanet, then that planet will exert a gravitational influence on the star that may be detectable from the earth by the apparent ``stellar wobble''---regular, periodic ...
<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>Current frontiers in complex stochastic modeling of high-dimensional processes include major emphases on so-called functional data: problems in which the data are snapshots of curves and surfaces representing fundamentally ...
<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>This thesis develops flexible non- and semiparametric Bayesian models for mixed continuous, ordered and unordered categorical data. These methods have a range of possible applications; the applications considered in ...
<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 ...