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An Algorithmic Framework for High Dimensional Regression with Dependent Variables
(20140224)We present an exploration of the rich theoretical connections between several classes of regularized models, network flows, and recent results in submodular function theory. This work unifies key aspects of these problems ... 
Applications of Robust Statistical Methods in Quantitative Finance
Financial asset returns and fundamental factor exposure data often contain outliers, observations that are inconsistent with the majority of the data. Both academic finance researchers and quantitative finance professionals ... 
Bayesian Methods for Graphical Models with Limited Data
Scientific studies in many fields involve understanding and characterizing dependence relationships among large numbers of variables. This can be challenging in settings where data is limited and noisy. Take survey data ... 
Bayesian Methods for Inferring Gene Regulatory Networks
The recent explosion in the availability of gene expression data has opened up new possibilities in advancing our understanding of the fundamental processes of life. To keep up with the increasing size of the datasets, new ... 
Bayesian Modeling For Multivariate Mixed Outcomes With Applications To Cognitive Testing Data
(20120913)This dissertation studies parametric and semiparametric approaches to latent variable models, multivariate regression and modelbased clustering for mixed outcomes. We use the term mixed outcomes to refer to binary, ordered ... 
Bayesian Modeling of a High Resolution Housing Price Index
Understanding how housing values evolve over time is important to consumers, real estate professionals, and policy makers. Existing methods for constructing housing indices are computed at a coarse spatial granularity, ... 
Bayesian Modeling of Health Data in Space and Time
(20130225)In recent years spatialtemporal modeling has become increasingly popular in the field of public health and epidemiology. Motivated by two datasets, we address three issues in the Bayesian modeling of health data in space ... 
Bayesian Nonparametric Inference of Effective Population Size Trajectories from Genomic Data
(20130725)Phylodynamics is an area at the intersection of phylogenetics and population genetics that aims to reconstruct population size trajectories from genetic data. Phylodynamic methods rely on a standard framework based on the ... 
Bayesian Population Reconstruction: A Method for Estimating Age and Sexspecific Vital Rates and Population Counts with Uncertainty from Fragmentary Data
(20130723)Current methods for reconstructing human populations of the past by age and sex are deterministic or do not formally account for measurement error. I propose “Bayesian reconstruction”, a method for simultaneously estimating ... 
Bayesian spatial and temporal methods for public health data
In this thesis, we develop flexible models to analyze public health data in time and/or in space. The development of our methodology is motivated by two examples: cancer incidence data in Washington State and birth outcome ... 
Bayesian spatial and temporal methods for public health data
In this thesis, we develop flexible models to analyze public health data in time and/or in space. The development of our methodology is motivated by two examples: cancer incidence data in Washington State and birth outcome ... 
Coevolution Regression and Composite Likelihood Estimation for Social Networks
We study how social networks and nodal attributes influence each other over time. A multiplicative coevolution regression (MCR) model is proposed for longitudinal network and nodal attribute data. The coevolution model is ... 
CoordinateFree Exponential Families on Contingency Tables
(20120913)We propose a class of coordinatefree multiplicative models on the set of positive distributions on contingency tables and on some sets of cells of a more general structure. The models are called relational and are generated ... 
Degeneracy, Duration, and Coevolution: Extending Exponential Random Graph Models (ERGM) for Social Network Analysis
We address three aspects of statistical methodology in the application of Exponential family Random Graphs to modeling social network processes. The first is the topic of model degeneracy in ERGMs. We show this is a ... 
Detecting and extracting complex patterns from images and realizations of spatial point processes
(2000)A common goal in the field of Computer Vision is the detection and extraction of patterns (e.g. lines, object boundaries) from binary image data . These images routinely occur as the product of edge detection algorithms, ... 
Discovering Interactions in Multivariate Time Series
In large collections of multivariate time series it is of interest to determine interactions between each pair of time series. Classically, interactions between time series have been studied using linear vector autoregressive ... 
DiscreteTime Threshold Regression for Survival Data with TimeDependent Covariates
A natural approach to survival analysis in many settings is to model the subject's ``health'' status as a latent stochastic process, where the terminal event is represented by the first time that the process crosses a ... 
Estimating Population Size Using the Network Scale Up Method
(20130725)We develop methods for estimating hardtoreach populations from data collected using networkbased questions on standard surveys. Such data arise by asking respondents how many people they know in a specific group (e.g. ... 
Estimation and Testing Following Model Selection
The field of postselection inference focuses on developing solutions for problems in which a researcher uses a single dataset to both identify a promising set of hypotheses and conduct statistical inference. One promising ... 
Finite Population Inference for Causal Parameters
Randomized experiments are often employed to determine whether a treatment X has a causal effect on an outcome Y. Under the NeymanRubin causal model with binary X and Y, each patient is characterized by two binary potential ...