Browsing Statistics by Title
Now showing items 39-58 of 108
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Generalized linear mixed models: development and comparison of different estimation methods
(2002)The use of generalized linear mixed models is growing in popularity in the modelling of correlated data. To date, methods available are either computationally intensive or asymptotically biased. The following work examines ... -
Genetic restoration on complex pedigrees
(1990)Analyses of genetic data observed on groups of related individuals frequently require the computation of probabilities on pedigrees. Existing methods are computationally intensive and can be infeasible on large and complex ... -
Geometric algorithms for interpretable manifold learning
This thesis proposes several algorithms in the area of interpretable unsupervised learning.Chapters 3 and 4 introduce a sparse convex regression approach for identifying local diffeomor- phisms from a dictionary of ... -
Gravimetric Anomaly Detection using Compressed Sensing
We address the problem of identifying underground anomalies (e.g. holes) based on gravity measurements. This is a theoretically well-studied yet difficult problem. In all except a few special cases, the inverse problem has ... -
Improving Uncertainty Quantification and Visualization for Spatiotemporal Earthquake Rate Models for the Pacific Northwest
The Pacific Northwest (PNW) has substantial earthquake risk, both due to the offshore Cascadia megathrust fault but also other fault systems that produce earthquakes under the region's population centers. Forecasts of ... -
Inference for High-Dimensional Instrumental Variables Regression
This thesis concerns statistical inference for the components of a high-dimensional regression parameter despite possible endogeneity of each regressor. Given a first-stage linear model for the endogenous regressors and a ... -
Inferring Network Structure From Partially Observed Graphs
Collecting social network data is notoriously difficult, meaning that indirectly observed or missing observations are very common. In this dissertation, We address two of such scenarios: inference on network measures without ... -
Interpretation and Validation for Unsupervised Learning
This thesis studies two major problems in unsupervised learning: manifold learning and clustering. The motivation of this research is to establish mathematically rigorous methods that enable practitioners to have better ... -
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, ...