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 15 of 15 for “"non-parametric models"”.
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Frequentist-Bayesian Hybrid Tests in Semi-parametric and Non-parametric Models with Low/High-Dimensional Covariate
… a test for the significant differences between non-parametric functions and the second one is to design a test allowing any departure of predictors of high dimensional X from constant. The implementation is also given in construction of the proposal test statistics for both problems. For the …
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Nuisance Parameter Estimation in Survival Models
… the interpretation of the hazard ratio is non-intuitive and it is commonly misinterpreted as a relative risk. This motivates alternative methods with more intuitive interpretations that avoid a reliance on the proportional hazards assumption. We explore censored quantile regression and …
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Analysis Using Smoothing Via Penalized Splines as Implemented in LME() in R
… provide a familiar framework for estimating semi-parametric and non-parametric models. Following a review of literature on splines and mixed models, details for implementing mixed model splines are presented. The examples use an experiment in the health sciences to demonstrate how to use mixed …
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An Examination into the Structure of Freight Rates in the Shipping Freight Markets
… process, as opposed to being stationary or non-stationary, as had previously been proposed; 2) does spot freight rate volatility also follow a fractionally integrated process; and 3) do freight rates exhibit conditional skewness and kurtosis? It then evaluates the impact that these factors …
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Population-wise consistent segmentation of diffusion weighted magnetic resonance images
… for manual labeling. We experiment with both parametric and non-parametric models for the gray matter and white matter segmentation problems, each model resulting in a different kind of atlas. Consistent population-wise segmentations require development of several integrated algorithms for …
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Non-parametric modelling of signals on graphs
… powerful when coupled with machine learning models, graphs pose unique challenges to those models, which need to be able to adapt to not only highly diverse data but also a highly diverse graph domain that may vary in size, connectivity patterns, and its interaction with node features, to …
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New techniques in low-Q² elastic electron-proton scattering measurements and the proton radius extraction
… the extraction of the proton charge radius using non-parametric models. We demonstrate that the kernel ridge regression and the Gaussian process have similar levels of performance compared to the traditional function fitting approaches. Our extracted values from different data sets still show the …
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Non-Parametric Spatial Models
… play a central role in spatial statistics. Parametric covariance functions have been used in most of the existing works on the analysis of spatial data. The primary reason for this is that the classes of parametric covariance functions guarantee that the fitted covariance function is …
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Sparse Gaussian Process Approximations and Applications
… distributions over functions. Gaussian process models require approximations in order to be practically useful. This thesis focuses on understanding existing approximations and investigating new ones tailored to specific applications. We advance the understanding of existing techniques first …
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Theoretical studies of gravitational lensing phenomena: the case of multiply imaged quasars
… mass distributions, the so-called ε-γ family of models (Wertz, Pelgrims & Surdej, 2012). After combining these results, it has allowed us to derive an expression for H0 independently of the model parameters. We have extended this study to the ε-γ family of models with external shear, as well as …
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Modelling growth patterns of bird species using non-linear mixed effects models
… methods such as polynomial regressions, non-parametric models and non-linear mixed effects models have been used to fit models to growth data. In recent years, non-linear mixed effects models have become an important tool for growth models. We have fitted univariate inverse exponential, …
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Essays on semi-/non-parametric methods in econometrics
… contains three chapters focusing on semi-/non-parametric models in econometrics. The first chapter, which is a joint work with Sukjin Han, considers parametric/semiparametric estimation and inference in a class of bivariate threshold crossing models with dummy endogenous variables. We …
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Data-Driven Policy Optimisation for Multi-Domain Task-Oriented Dialogue
… deep reinforcement learning performs on par with non-parametric models even in a low data regime while significantly reducing the computational complexity compared with the previous state-of-the-art. The deployment of a dialogue manager without any pre-training on human conversations is not a …
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Shape Dynamical Models for Activity Recognition and Coded Aperture Imaging for Light-Field Capture
… are some examples of patterns that are dynamic. Models and algorithms to study these patterns must take into account the dynamics of these patterns while exploiting the classical pattern recognition techniques. The first part of this dissertation is an attempt to model and recognize such …