Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 19 of 19 for “"Nonparametrics"”.
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Truncated Bayesian nonparametrics
… increase over time. Priors from Bayesian nonparametrics are well-suited to this modeling challenge: they generate a countably infinite number of underlying traits, which allows the number of expressed traits to both be random and to grow with the dataset size. We also require corresponding …
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Essays in Econometrics: Nonparametrics and Robustness
This thesis consists of three chapters. In each chapter I consider a particular problem in econometrics with implications for applied research, and in each case I attempt to solve that problem. In Chapter 1 I consider the task of inferring causal effects when only `proxy controls' are available. …
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Modeling Temporal and Spatial Data Dependence with Bayesian Nonparametrics
<p>In this thesis, temporal and spatial dependence are considered within nonparametric priors to help infer patterns, clusters or segments in data. In traditional nonparametric mixture models, observations are usually assumed exchangeable, even though dependence often exists associated with the …
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Improved prediction and optimal sequencing strategies for genomic variant discovery via Bayesian nonparametrics
Despite the advent of Big Data, data-gathering in many domains can still be an expensive process that necessitates careful planning when operating under a fixed, limited budget. For instance, sequencing new genomic data is a complex procedure that requires careful tuning: researchers can spend …
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Multiagent planning with Bayesian nonparametric asymptotics
… the environment in which a system acts. Bayesian nonparametrics, on the other hand, possess structural flexibility beyond the capabilities of past parametric techniques commonly used in planning systems. This extra flexibility comes at the cost of increased computational cost, which has prevented …
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Nonparametric Bayesian Dictionary Learning and Count and Mixture Modeling
… approaches of statistical modeling. Bayesian nonparametrics constitute a promising research direction, in that such techniques can fit the data with a model that can grow with complexity to match the data. In this dissertation we consider nonparametric Bayesian modeling with completely random …
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Toward Faster Methods in Bayesian Unsupervised Learning
… “label-switching problem”. Secondly, in Bayesian nonparametrics for unsupervised learning, computers cannot learn the distribution over the countable infinity of random variables posited by the model in f inite time. Finally, to assess the generalizability of Bayesian conclusions, we might want to …
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A study of relationships between organizational characteristics and QR adoption in the U.S. apparel industry
… (n=103). Regression, discriminant analysis, and nonparametrics were used to test the statistical significance of hypothesized relationships. The most frequently used technologies were small lot orders, short cycle cut planning, short cycle sewing, and production planning with customer. Firm size, …
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Inference of Low-Dimensional Latent Structure in High-Dimensional Data
… dynamic data, and documents with Bayesian nonparametrics. The thesis consists of three parts.</p><p>First, nonparametric Bayesian methods are considered for recovery of imagery based upon compressive measurements. A truncated beta-Bernoulli process is employed to infer an appropriate …
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Bayesian Applications in Financial Econometrics
This thesis consists of three chapters in Bayesian financial econometrics. The three chapters apply both Bayesian nonparametric and parametric methods to financial market and macroeconomic time series. Chapter 1 extends popular discrete time short-rate models to include Markov switching of infinite …
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Use and Development of Matrix Factorisation Techniques in the Field of Brain Imaging
Matrix factorisation treats observations as linear combinations of basis vectors together with, possibly, additive noise. Notable techniques in this family are Principal Components Analysis and Independent Components Analysis. Applied to brain images, matrix factorisation provides insight into the …
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The Cauchy-Net Mixture Model for Clustering with Anomalous Data
We live in the data explosion era. The unprecedented amount of data offers a potential wealth of knowledge but also brings about concerns regarding ethical collection and usage. Mistakes stemming from anomalous data have the potential for severe, real-world consequences, such as when building …
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A New Nonparametric Procedure for the k-sample Problem
The k-sample data setting is one of the most common data settings used today. The null hypothesis that is most generally of interest for these methods is that the k-samples have the same location. Currently there are several procedures available for the individual who has data of this type. The …
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Bayesian Nonparametric Modeling and Theory for Complex Data
<p>The dissertation focuses on solving some important theoretical and methodological problems associated with Bayesian modeling of infinite dimensional `objects', popularly called nonparametric Bayes. The term `infinite dimensional object' can refer to a density, a conditional density, a regression …
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Some Recent Advances in Non- and Semiparametric Bayesian Modeling with Copulas, Mixtures, and Latent Variables
<p>This thesis develops flexible non- and semiparametric Bayesian models for mixed continuous, ordered and unordered categorical data. These methods have a range of possible applications; the applications considered in this thesis are drawn primarily from the social sciences, where multivariate, …
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Tree-based Methods for Learning Probability Distributions
<p>Learning probability distributions is a fundamental inferential task in statistics but challenging if a data distribution of our interest is complicated and high-dimensional. Addressing this challenging problem is the main topic of this thesis, and mainly discussed herein are two types of new …
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Contributions to asymptotic theory in nonparametric statistics
Nonparametric statistics, in which the parameter of interest is allowed to be infinite-dimensional, provides a theoretical lens through which to analyse the performance of modern data science and machine learning methods. The need for a rigorous understanding of these procedures has never been …
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Bayesian Time Series Learning with Gaussian Processes
The analysis of time series data is important in fields as disparate as the social sciences, biology, engineering or econometrics. In this dissertation, we present a number of algorithms designed to learn Bayesian nonparametric models of time series. The goal of these kinds of models is twofold. …
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Probabilistic modelling of somatic alterations in bulk tissue and single cells using repeat DNA
Chromosomal instability characterises several cancer types, in which large-scale structural alterations of the genome accumulate at an increased rate. An important class of structural alterations are somatic copy number alterations (SCNAs). SCNAs have been shown to be major drivers of oncogenesis …