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Now showing items 47-66 of 108
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Large-Scale B Cell Receptor Sequence Analysis Using Phylogenetics and Machine Learning
The adaptive immune system synthesizes antibodies, the soluble form of B cell receptors (BCRs), to bind to and neutralize pathogens that enter our body. B cells are able to generate a diverse set of high affinity antibodies ... -
Latent models for cross-covariance
(2001)Cross-covariance problems arise in the analysis of multivariate data that can be divided naturally into two blocks of variables, X and Y, observed on the same units. In a cross-covariance problem we are interested, not in ... -
Latent Variable Models for Indirectly or Imprecisely Measured Networks
In many scientific settings, networks are important structures used to represent the relationships between actors in a population of study. The most common methods for measuring networks are to survey study participants ... -
Learning and Manifolds: Leveraging the Intrinsic Geometry
(2013-07-23)In this work, we explore and exploit the use of differential operators on manifolds - the Laplace-Beltrami operator in particular - in learning tasks. In particular, we are interested in uncovering the geometric structure ... -
Likelihood Analysis of Causal Models
We analyze several problems in causal inference from the perspective of maximum likelihood. Two archetypal likelihoods are primarily concerned: Gaussian likelihood for continuous data and multinomial likelihood for discrete ... -
The Likelihood Pivot: Performing Inference with Confidence
Maximum likelihood estimation is a popular statistical method. To account for possible model misspecification, the sandwich estimate of variance can be used to generate asymptotically correct confidence intervals. Several ... -
Likelihood-based haplotype frequency modeling using variable-order Markov chains
The localized haplotype-cluster model uses variable-order Markov chains (VOMCs) to create an empirical model for haplotype probabilities that adapts to the changing structure of linkage disequilibrium (LD) across the genome. ... -
Likelihood-Based Inference for Partially Observed Multi-Type Markov Branching Processes
Markov branching processes are a class of continuous-time Markov chains (CTMCs) frequently used in stochastic modeling with ubiquitous applications. Bivariate or multi-type processes are necessary to model phenomena such ... -
Linear Structural Equation Models with Non-Gaussian Errors: Estimation and Discovery
Linear structural equation models (SEMs) are multivariate models which encode direct causal effects. We focus on SEMs in which unobserved latent variables have been marginalized and only observed variables are explicitly ... -
Lord's Paradox and Targeted Interventions: The Case of Special Education
Lord (1967) describes a hypothetical “paradox” in which two statisticians, analyzing the same dataset using different but defensible methods, come to very different conclusions about the effects of an intervention on student ... -
Maximum likelihood estimation in Gaussian AMP chain graph models and Gaussian ancestral graph models
(2004)Graphical Markov models use graphs to represent dependencies between stochastic variables. Via Markov properties, missing edges in the graph are translated into conditional independence statements, which, in conjunction ... -
Methods for estimation and inference for high-dimensional models
This thesis tackles three different problems in high-dimensional statistics. The first two parts of the thesis focus on estimation of sparse high-dimensional undirected graphical models under non-standard conditions, ... -
Methods for the Statistical Analysis of Preferences, with Applications to Social Science Data
Preference data, such as rankings and ratings, are prevalent in the social sciences for expressing and measuring attitudes or opinions. Oftentimes, deterministic algorithms or summary statistics are used to aggregate ... -
Methods, Models, and Interpretations for Spatial-Temporal Public Health Applications
Improving the health of communities and individuals around the world is one of the great challenges of this densely connected global era which finds itself rife with disparity. In order to make the best use of our limited ... -
Metric Learning for Hermitian Manifolds
In recent years, manifold learning has emerged as one of the most promising approaches for performing nonparametric dimension reduction. While numerous manifold learning algorithms of varying degrees of complexity have ... -
Missing Data Methods for Observational Health Dataset
This dissertation is motivated by missing data problems arising from two observational health datasets. The first dataset is created by the SWOG study that linked medicare claims to a prostate cancer prevention trial ... -
Mixture models to fit heavy-tailed, heterogeneous or sparse data
With the advent of modern technologies, many scientific fields collect and analyze increasingly large datasets. Unfortunately, the complexity and heterogeneity of these datasets cannot be properly captured through classical ... -
Model-Based Penalized Regression
This thesis contains three chapters that consider penalized regression from a model-based perspective, interpreting penalties as assumed prior distributions for unknown regression coefficients. In the first chapter, we ... -
Modeling Heterogeneity within and between Matrices and Arrays
(2013-11-14)Datasets in the form of matrices and arrays arise frequently in the social and biological sciences and are characterized by measurements indexed by two or more factors. In this dissertation we address two problems relating ... -
Monte Carlo estimation of identity by descent in populations
Genetic similarity between organisms arises from segments of shared genome, which are said to be identical by descent (IBD). Modeling IBD in pedigrees forms the basis of classical linkage analysis and has been a fruitful ...