Browsing Statistics by Title
Now showing items 1-20 of 108
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A Stochastic Neural-Network Parameterization for Coarse-grid Climate Models
Coarse-grid climate models require parameterizations to include the effect of unresolved sub-grid processes. Recently, machine learning approaches have shown promise in producing more accurate parameterizations than existing ... -
Addressing double dipping through selective inference and data thinning
While classical statistical methods assume that we only ever test pre-specified hypotheses about pre-specified models, the reality is that scientists often explore their data before coming up with models and hypotheses of ... -
An Algorithmic Framework for High Dimensional Regression with Dependent Variables
(2014-02-24)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 Hierarchical Models and Moment Bounds for High-Dimensional Time Series
In this dissertation, I explore two statistical tasks involving high-dimensional time series.The first task is to forecast high-dimensional time series using Bayesian hierarchical models (BHM). The data under modeling is ... -
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 methods for variable selection
Choosing a statistical model and accounting for uncertainty about this choice are important parts of the scientific process and are required for common statistical tasks such as parameter estimation, interval estimation, ... -
Bayesian Modeling For Multivariate Mixed Outcomes With Applications To Cognitive Testing Data
(2012-09-13)This dissertation studies parametric and semiparametric approaches to latent variable models, multivariate regression and model-based 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
(2013-02-25)In recent years spatial-temporal 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 Models in Population Projections and Climate Change Forecast
The goal of this dissertation is to develop new methods for probabilistic projections in demography and climate science.In the first project, I propose new models for improving estimates and projections of total fertility ... -
Bayesian Nonparametric Inference of Effective Population Size Trajectories from Genomic Data
(2013-07-25)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 Sex-specific Vital Rates and Population Counts with Uncertainty from Fragmentary Data
(2013-07-23)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 ... -
Bias Modeling for Integrating Digital Data and Conventional Surveys for Migration Estimation
Obtaining reliable and timely estimates of migration flows is critical for advancing migration theory and guiding policy decisions, but it remains a challenge. Digital data provide granular information on time and space ... -
Causal Structure Learning in High Dimensions
Directed graphical models are commonly used to model causal relations between random variables and to understand conditional independencies in their joint distributions. We focus on the crucial task of structure learning, ... -
Causality, Fairness, and Information in Peer Review
In this dissertation, I study peer review---the process by which scientists evaluate one another's work for publication or funding---through three distinct but related lenses. I focus on multi-step grant proposal peer ... -
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 ...