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- Bayesian variable selection (1)
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- Post-selection inference (1)
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Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Wavelet Coherence Analysis With An Application Of Brain Images, Yiqian Fang
Wavelet Coherence Analysis With An Application Of Brain Images, Yiqian Fang
Arts & Sciences Electronic Theses and Dissertations
Wavelet analysis has become an emerging method in a wide range of applications with non-stationary data. In this work, we apply wavelets to tackle the problem of estimating dynamic association in a collection of multivariate non-stationary time series. Coherence is a common metric for linear dependence across signals. However, it assumes static dependence and does not sufficiently model many biological processes with time-evolving dependence structures. We explore continuous wavelet analysis for modeling and estimating such dynamic dependence under the replicated multivariate time series settings. Wavelet transformation provides a decomposition of signals that localizes in both time and frequency domains, hence …
Bayesian Posterior Inference And Lan For L̩Vy Models Under High-Frequency Data, Qi Wang
Bayesian Posterior Inference And Lan For L̩Vy Models Under High-Frequency Data, Qi Wang
Arts & Sciences Electronic Theses and Dissertations
Parameter estimation and inference for L̩vy models under high-frequency data has been an exciting and important task in the field of financial mathematics and has been found practically useful when analyzing real financial data. One feature of L̩vy models is the allowance of jumps to model the abrupt changes sometimes observed in the market. In this thesis, we discuss some problems related to the statistical inference of L̩vy models based on high-frequency data emphasizing on the presence of the jumps. The first problem we consider focuses on the estimation of the volatility, which is critical to measure and control the …
Bayesian Variable Selection And Post-Selection Inference, Qiyiwen Zhang
Bayesian Variable Selection And Post-Selection Inference, Qiyiwen Zhang
Arts & Sciences Electronic Theses and Dissertations
In this dissertation, we first develop a novel perspective to compare Bayesian variable selection procedures in terms of their selection criteria as well as their finite-sample properties. Secondly, we investigate Bayesian post-selection inference in two types of selection problems: linear regression and population selection. We will demonstrate that both inference problems are susceptible to selection effects since the selection procedure is data-dependent. Before comparing Bayesian variable selection procedures, we first classify the current Bayesian variable selection procedures into two classes: those with selection criteria defined on the space of candidate models, and those with selection criteria not explicitly formulated on …
Multi-Omics Integration For Gene Fusion Discovery And Somatic Mutation Haplotyping In Cancer, Steven Mason Foltz
Multi-Omics Integration For Gene Fusion Discovery And Somatic Mutation Haplotyping In Cancer, Steven Mason Foltz
Arts & Sciences Electronic Theses and Dissertations
Cancer is a disease caused by changes to the genome and dysregulation of gene expression. Among many types of mutations, including point mutations, small insertions and deletions, large scale structural variants, and copy number changes, gene fusions are another category of genomic and transcriptomic alteration that can lead to cancer and which can serve as therapeutic targets. We studied gene fusion events using data from The Cancer Genome Atlas, including over 9,000 patients from 33 cancer types, finding patterns of gene fusion events and dysregulation of gene expression within and across cancer types. With data from the CoMMpass study (Multiple …
Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data, Seunghwan Kim
Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data, Seunghwan Kim
McKelvey School of Engineering Theses & Dissertations
Electronic Health Records (EHR) are widely adopted and used throughout healthcare systems and are able to collect and store longitudinal information data that can be used to describe patient phenotypes. From the underlying data structures used in the EHR, discrete data can be extracted and analyzed to improve patient care and outcomes via tasks such as risk stratification and prospective disease management. Temporality in EHR is innately present given the nature of these data, however, and traditional classification models are limited in this context by the cross- sectional nature of training and prediction processes. Finding temporal patterns in EHR is …